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The impact of public health insurance on health care utilisation, financial protection and health status in low- and middle-income countries: A systematic review
1 Department of Health Sciences, University of York, York, England, United Kingdom
2 Centre of Health Economics, University of York, York, England, United Kingdom
3 Luxembourg Institute of Socio-economic Research (LISER), Luxembourg
4 Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
The search strategy for this review is available in Supporting Information files.
Expanding public health insurance seeks to attain several desirable objectives, including increasing access to healthcare services, reducing the risk of catastrophic healthcare expenditures, and improving health outcomes. The extent to which these objectives are met in a real-world policy context remains an empirical question of increasing research and policy interest in recent years.
We reviewed systematically empirical studies published from July 2010 to September 2016 using Medline, Embase, Econlit, CINAHL Plus via EBSCO, and Web of Science and grey literature databases. No language restrictions were applied. Our focus was on both randomised and observational studies, particularly those including explicitly attempts to tackle selection bias in estimating the treatment effect of health insurance. The main outcomes are: (1) utilisation of health services, (2) financial protection for the target population, and (3) changes in health status.
8755 abstracts and 118 full-text articles were assessed. Sixty-eight studies met the inclusion criteria including six randomised studies, reflecting a substantial increase in the quantity and quality of research output compared to the time period before 2010. Overall, health insurance schemes in low- and middle-income countries (LMICs) have been found to improve access to health care as measured by increased utilisation of health care facilities (32 out of 40 studies). There also appeared to be a favourable effect on financial protection (26 out of 46 studies), although several studies indicated otherwise. There is moderate evidence that health insurance schemes improve the health of the insured (9 out of 12 studies).
Increased health insurance coverage generally appears to increase access to health care facilities, improve financial protection and improve health status, although findings are not totally consistent. Understanding the drivers of differences in the outcomes of insurance reforms is critical to inform future implementations of publicly funded health insurance to achieve the broader goal of universal health coverage.
In recent decades, achieving universal health coverage (UHC) has been a major health policy focus globally.[ 1 – 3 ] UHC entitles all people to access healthcare services through publicly organised risk pooling,[ 4 ] safeguarding against the risk of catastrophic healthcare expenditures.[ 5 ] Low- and middle-income countries (LMICs) face particular challenges in achieving UHC due to particularly limited public resources for health care, inefficient allocation, over-reliance on out-of-pocket payments, and often large population size.[ 5 ] As a result, access to health care and the burden of financial cost in LMICs tends to be worse for the poor, often resulting in forgone care.[ 6 – 8 ]
Introducing and increasing the coverage of publicly organised and financed health insurance is widely seen as the most promising way of achieving UHC,[ 9 , 10 ] since private insurance is mostly unaffordable for the poor.[ 11 ] Historically, social health insurance, tax-based insurance, or a mix of the two have been the dominant health insurance models amongst high income countries and some LMICs, including Brazil, Colombia, Costa Rica, Mexico, and Thailand.[ 12 ] This is partly influenced by the size of the formal sector economy from which taxes and payroll contributions can be collected. In recent decades, community-based health insurance (CBHI) or “mutual health organizations” have become increasingly popular among LMICs, particularly in Sub-Saharan Africa (e.g. Burkina Faso,[ 13 ] Senegal[ 14 ] and Rwanda[ 15 ]) as well as Asia (e.g. China[ 16 ] and India[ 17 ]). CBHI has emerged as an alternative health financing strategy, particularly in cases where the public sector has failed to provide adequate access to health care.[ 18 ]
We searched for existing systematic reviews on health insurance in the Cochrane Database for Systematic Reviews, Medline, Embase, and Econlit. Search terms “health insurance”, “low-middle income countries”, and “utilisation” were used alongside methodological search strategy to locate reviews. Seven systematic reviews were identified of varying levels of quality, [ 19 – 26 ] with Acharya et al.[ 27 ] being the most comprehensive. The majority of existing reviews has suggested that publicly-funded health insurance has typically shown a positive impact on access to care, while the picture for financial protection was mixed, and evidence of the impact on health status was very sparse.
This study reviews systematically the recent fast-growing evidence on the impact of health insurance on health care utilisation, financial protection and health status in LMICs. Since the publication of Acharya et al. (which conducted literature searches in July 2010), the empirical evidence on the impact of health insurance has expanded significantly in terms of quantity and quality, with growing use of sophisticated techniques to account for statistical challenges[ 28 ] (particularly insurance selection bias). This study makes an important contribution towards our understanding of the impact of health insurance in LMICs, taking particular care in appraising the quality of studies. We recognise the heterogeneity of insurance schemes implemented in LMICs and therefore do not attempt to generalise findings, but we aim to explore the pattern emerging from various studies and to extract common factors that may affect the effectiveness of health insurance, that should be the focus of future policy and research. Furthermore, we explore evidence of moral hazard in insurance membership, an aspect that was not addressed in the Acharya et al review.[ 27 ]
This review was planned, conducted, and reported in adherence with PRISMA standards of quality for reporting systematic reviews.[ 29 ]
Studies focusing on LMICs are included, as measured by per capita gross national income (GNI) estimated using the World Bank Atlas method per July 2016.[ 30 ]
Classification of health insurance can be complicated due to the many characteristics defining its structure, including the mode of participation (compulsory or voluntary), benefit entitlement, level of membership (individual or household), methods for raising funds (taxes, flat premium, or income-based premium) and the mechanism and extent of risk pooling [ 31 ]. For the purpose of this review, we included all health insurance schemes organised by government, comprising social health insurance and tax-based health insurance. Private health insurance was excluded from our review, but we recognise the presence of community-based health insurance (CBHI) in many LMICs, especially in Africa and Asia [ 18 ]. We also therefore included CBHI if it was scaled up nationally or was actively promoted by national government. Primary studies that included both public and private health insurance were also considered for inclusion if a clear distinction between the two was made in the primary paper. Studies examining other types of financial incentives to increase the demand for healthcare services, such as voucher schemes or cash transfers, were excluded.
In order to provide robust evidence on the effect on insurance, it is necessary to compare an insured group with an appropriate control group. In this review, we selected studies that used an uninsured population as the control group. Multiple comparison groups were allowed, but an uninsured group had to be one of them.
We focus on three main outcomes:
- Utilisation of health care facilities or services (e.g. immunisation coverage, number of visits, rates of hospitalisation).
- Financial protection, as measured by changes in out-of-pocket (OOP) health expenditure at household or individual level, and also catastrophic health expenditure or impoverishment from medical expenses.
- Health status, as measured by morbidity and mortality rates, indicators of risk factors (e.g. nutritional status), and self-reported health status.
The scope of this review is not restricted to any level of healthcare delivery (i.e. primary or secondary care). All types of health services were considered in this review.
Types of studies
The review includes randomized controlled trials, quasi-experimental studies (or “natural experiments”[ 32 ]), and observational studies that account for selection bias due to insurance endogeneity (i.e. bias caused by insurance decisions that are correlated with the expected level of utilisation and/or OOP expenditure). Observational studies that did not take account of selection bias were excluded.
Databases and search terms
A search for relevant articles was conducted on 6 September 2016 using peer-reviewed databases (Medline, Embase, Econlit, CINAHL Plus via EBSCO and Web of Science) and grey literatures (WHO, World Bank, and PAHO). Our search was restricted to studies published since July 2010, immediately after the period covered by the earlier Acharya et al. (2012) review. No language restrictions were applied. Full details of our search strategy are available in the supporting information ( S1 Table ).
Screening and data extraction
Two independent reviewers (DE and MS) screened all titles and abstracts of the initially identified studies to determine whether they satisfied the inclusion criteria. Any disagreement was resolved through mutual consensus. Full texts were retrieved for the studies that met the inclusion criteria. A data collection form was used to extract the relevant information from the included studies.
Assessment of study quality
We used the Grades of Assessment, Development and Evaluation (GRADE) system checklist[ 33 , 34 ] which is commonly used for quality assessment in systematic reviews. However, GRADE does not rate observational studies based on whether they controlled for selection bias. Therefore, we supplemented the GRADE score with the ‘Quality of Effectiveness Estimates from Non-randomised Studies’ (QuEENS) checklist.[ 35 ]
cRandomised studies were considered to have low risk of bias. Non-randomised studies that account for selection on observable variables, such as propensity score matching (PSM), were categorised as high risk of bias unless they provided adequate assumption checks or compared the results to those from other methods, in which case they may be classed as medium risk. Non-randomised studies that account for selection on both observables and unobservables, such as regression with difference-in-differences (DiD) or Heckman sample selection models, were considered to have medium risk of bias–some of these studies were graded as high or low risk depending on sufficiency of assumption checks and comparison with results from other methods.
Heterogeneity of health insurance programmes across countries and variability in empirical methods used across studies precluded a formal meta-analysis. We therefore conducted a narrative synthesis of the literature and did not report the effect size. Throughout this review, we only considered three possible effects: positive outcome, negative outcome, or no statistically significant effect (here defined as p-value > 0.1).
Results of the search
Our database search identified 8,755 studies. Five additional studies were retrieved from grey literature. After screening of titles and abstracts, 118 studies were identified as potentially relevant. After reviewing the full-texts, 68 studies were included in the systematic review (see Fig 1 for the PRISMA diagram). A full description of the included studies is presented in the supporting information ( S2 Table ). Of the 68 included studies, 40 studies examined the effect on utilisation, 46 studies on financial protection, and only 12 studies on health status (see Table 1 ).
* QUEENS score: 1 = high risk of bias; 2 = moderate risk; 3 = low risk; GRADE score: Low = low quality; Moderate = moderate quality; High = high quality
† Positive effect for financial protection means that health insurance decreases out-of-pocket health expenditure or reduces the event of catastrophic health expenditure
Utilisation of health care
Table 2 collates evidence on the effects of health insurance on utilisation of healthcare services. Three main findings were observed:
* SHI = Social Health Insurance; CBHI = Community-based Health Insurance
** Queens score: 1 = high risk of bias; 2 = moderate risk; 3 = low risk
† Grade score: Low = low quality; Moderate = moderate quality; High = high quality
- Evidence on utilisation of curative care generally suggested a positive effect, with 30 out of 38 studies reporting a statistically significant positive effect.
- Evidence on preventive care is less clear with 4 out of 7 studies reporting a positive effect, two studies finding a negative effect and one study reporting no effect.
- Among the higher quality studies, i.e. those that suitably controlled for selection bias reflected by moderate or low GRADE score and low risk of bias (score = 3) on QuEENS, seven studies reported a positive relationship between insurance and utilisation. One study[ 36 ] reported no statistically significant effect, and another study found a statistically significant negative effect.[ 37 ]
Overall, evidence on the impact of health insurance on financial protection is less clear than that for utilisation (see Table 3 ). 34 of the 46 studies reported the impact of health insurance on the level of out-of-pocket health expenditure. Among those 34 studies, 17 found a positive effect (i.e. a reduction in out-of-pocket expenditure), 15 studies found no statistically significant effect, and two studies–from Indonesia[ 59 ] and Peru[ 62 ]–reported a negative effect (i.e. an increase in out-of-pocket expenditure).
** Queens score: 1 = high risk; 2 = moderate risk; 3 = low risk
Another financial protection measure is the probability of incurring catastrophic health expenditure defined as OOP exceeding a certain threshold percentage of total expenditure or income. Of the 14 studies reporting this measure, nine reported reduction in the risk of catastrophic expenditure, three found no statistically significant difference, and two found a negative effect of health insurance. Only four studies reported sensitivity analysis varying changes in the threshold level,[ 59 , 62 , 75 , 76 ] though this did not materially affect the findings.
- Two studies used a different measure of financial protection, the probability of impoverishment due to catastrophic health expenditure, reporting conflicting findings.[ 77 , 78 ] Finally, four studies evaluated the effect on financial protection by assessing the impact of insurance on non-healthcare consumption or saving behaviour, such as non-medical related consumption[ 79 ], probability of financing medical bills via asset sales or borrowing[ 40 ], and household saving[ 80 ]. No clear pattern can be observed from those four studies.
Improving health is one of the main objectives of health insurance, yet very few studies thus far have attempted to evaluate health outcomes. We identified 12 studies, with considerable variation in the precise health measure considered (see Table 4 ). There was some evidence of positive impact on health status: nine studies found a positive effect, one study reported a negative effect, and two studies reported no effect.
Type of insurance and countries
Considering the heterogeneity of insurance schemes among different countries, we attempted to explore the aggregate results by the type of insurance scheme and by country. Table 5 provides a summary of results classified by three type of insurance scheme: community-based health insurance, voluntary health insurance (non-CBHI), and compulsory health insurance. This division is based on the mode of participation (compulsory vs voluntary), which may affect the presence of adverse selection and moral hazard. Premiums are typically community-rated in CBHI, risk-rated in voluntary schemes and income-rated in compulsory schemes.
In principle, CBHI is also considered a voluntary scheme, but we separated it to explore whether the larger size of pooling from non-CBHI schemes may affect the outcomes. Social health insurance is theoretically a mandatory scheme that requires contribution from the enrolees. However, in the context of LMICs, the mandatory element is hard to enforce, and in practice the scheme adopts a voluntary enrolment. Additionally, the government may also want to subsidise the premium for poor people. Therefore, in this review SHI schemes can fall into either the voluntary health insurance (non-CBHI) or compulsory health insurance (non-CBHI), depending on the target population defined in the evaluation study. Lastly, we chose studies with high quality/low risk only to provide more robust results.
Based on the summary in Table 5 , the effect on utilisation overall does not differ based on type of insurance, with most evidence suggesting an overall increase in utilisation by the insured. The two studies showing no effect or reduced consumption of care were conducted in two different areas of India, which may–somewhat tentatively–suggest a common factor unique to India’s health system that may compromise the effectiveness of health insurance in increasing utilisation.
Regarding financial protection, the evidence for both CBHI and non-CBHI voluntary health insurance is inconclusive. Furthermore, there is an indication of heterogeneity by supply side factors captured by proximity to health facilities. Evidence from studies exploring subsidised schemes suggests no effect on financial protection, even a negative effect among the insured in Peru.
Lastly, evidence for health status may be influenced by how health outcomes are measured. Studies exploring specific health status, (examples included health indexes, wasting, C-reactive protein, and low birth weight), show a positive effect, whereas studies using mortality rates tends to show no effect or even negative effects. Studies exploring CBHI scheme did not find any evidence of positive effect on health status, as measured either by mortality rate or specific health status.
This review synthesises the recent, burgeoning empirical literature on the impact of health insurance in LMICs. We identified a total of 68 eligible studies over a period of six years–double the amount identified by the previous review by Acharya et al. over an approximately 60-year time horizon (1950—July 2010). We used two quality assessment checklists to scrutinise the study methodology, taking more explicit account of the methodological robustness of non-experimental designs.
Programme evaluation has been of interest to many researchers for reporting on the effectiveness of a public policy to policymakers. In theory, the gold standard for a programme evaluation is the randomised control trial, in which the treatment is randomly assigned to the participants. The treatment assignment process has to be exogenous to ensure that any observed effect between the treated and control groups can only be caused by the difference in the treatment assignment. Unfortunately, this ideal scenario is often not feasible in a public policy setting. Our findings showed that only three papers between 2010 and 2016 were able to conduct a randomised study to evaluate the impact of health insurance programmes in developing countries, particularly CBHI [ 38 , 75 , 103 ]. Policymakers may believe in the value of an intervention regardless of its actual evidence base, or they may believe that the intervention is beneficial and that no one in need should be denied it. In addition, policymakers are inclined to demonstrate the effectiveness of an intervention that they want implemented in the most promising contexts, as opposed to random allocation [ 104 ].
Consequently, programme evaluators often have to deal with a non-randomised treatment assignment which may result in selection bias problems. Selection bias is defined as a spurious relationship between the treatment and the outcome of interest due to the systematic differences between the treated and the control groups [ 105 ]. In the case of health insurance, an individual who chooses to enrol in the scheme may have different characteristics to an individual who chooses not to enrol. When those important characteristics are unobservable, the analyst needs to apply more advanced techniques and, sometimes, stronger assumptions. Based on our findings, we noted several popular methods, including propensity score matching (N = 8), difference-in-difference (N = 10), fixed or random effects of panel data (N = 6), instrumental variables (N = 12) and regression discontinuity (N = 6). Those methods have varying degree of success in controlling the unobserved selection bias and analysts should explore the robustness of their findings by comparing initial findings with other methods by testing important assumptions. We noted some papers combining two common methods, such as difference-in-difference with propensity score matching (N = 10) and fixed effects with instrumental variables (N = 8), in order to obtain more robust results.
Compared with the earlier review, our study has found stronger and more consistent evidence of positive effects of health insurance on health care utilisation, but less clear evidence on financial protection. Restricting the evidence base to the small subset of randomised studies, the effects on financial protection appear more consistently positive, i.e. three cluster randomised studies[ 39 , 75 , 76 ] showed a decline in OOP expenditure and one randomised study[ 36 ] found no significant effect.
Besides the impact on utilisation and financial protection, this review identified a number of good quality studies measuring the impact of health insurance on health outcomes. Twelve studies were identified (i.e. twice as many as those published before 2010), nine of which showed a beneficial health effect. This holds for the subset of papers with stronger methodology for tackling selection bias.[ 39 , 49 , 89 , 103 ] In cases where a health insurance programme does not have a positive effect on either utilisation, financial protection, and health status, it is particularly important to understand the underlying reasons.
Possible explanation of heterogeneity
Heterogeneity of the impact of health insurance may be explained by differences in health systems and/or health insurance programmes. Robyn et al. (2012) and Fink et al (2013) argued that the lack of significant effect of insurance in Burkina Faso may have been partially influenced by the capitation payment system. As the health workers relied heavily on user fees for their income, the change of payment system from fee-for-services to capitation may have discouraged provision of high quality services. If enrolees perceive the quality of contracted providers as bad, they might delay seeking treatment, which in turn could impact negatively on health.
Several studies from China found the utilisation of expensive treatment and higher-level health care facilities to have increased following the introduction of the insurance scheme.[ 41 , 44 , 45 , 88 ] A fee-for-service payment system may have incentivised providers to include more expensive treatments.[ 43 , 83 , 88 ] Recent systematic reviews suggested that payment systems might play a key role in determining the success of insurance schemes,[ 23 , 106 ] but this evidence is still weak, as most of the included studies were observational studies that did not control sufficiently for selection bias.
Uncovered essential items
Sood et al. (2014) found no statistically significant effect of community-based health insurance on utilisation in India. They argued that this could be caused by their inability to specify the medical conditions covered by the insurance, causing dilution of a potential true effect. In other countries, transportation costs[ 69 ] and treatments that were not covered by the insurance[ 59 , 60 ] may explain the absence of a reduction in out-of-pocket health expenditures.
Two studies in Georgia evaluated the same programme but with different conclusions.[ 50 , 51 ] This discrepancy may be explained by the difference in the estimated treatment effect: one used average treatment effect (ATE), finding no effect, and another used average treatment effect on the treated (ATT), reporting a positive effect. ATE is of prime interest when policymakers are interested in scaling up the programme, whereas ATT is useful to measure the effect on people who were actually exposed to insurance.[ 107 ]
Duration of health insurance
We also found that the longer an insurance programme has been in place prior to the timing of the evaluation, the higher the odds of improved health outcomes. It is plausible that health insurance would not change the health status of population instantly upon implementation.[ 21 ] While there may be an appetite among policymakers to obtain favourable short term assessments, it is important to compare the impact over time, where feasible.
Acharya et al (2012) raised an important question about the possibility of a moral hazard effect as an unintended consequence of introducing (or expanding) health insurance in LMICs. We found seven studies exploring ex-ante moral hazard by estimating the effect on preventive care. If uninsured individuals expect to be covered in the future, they may reduce the consumption of preventive care or invest less in healthy behaviours.[ 108 , 109 ] Current overall evidence cannot suggest a definite conclusion considering the heterogeneity in chosen outcomes. One study found that the use of a self-treated bed nets to prevent malaria declined among the insured group in Ghana[ 54 ] while two studies reported an increase in vaccination rates[ 62 ] and the number of prenatal care visits[ 55 , 62 ]among the insured group. Another study reported no evidence that health insurance encouraged unhealthy behaviour or reduction of preventive efforts in Thailand.[ 66 ]
Two studies from Colombia found that the insured group is more likely to increase their demand for preventive treatment.[ 47 , 49 ] As preventive treatment is free for all, both authors attributed this increased demand to the scheme’s capitation system, incentivising providers to promote preventive care to avoid future costly treatments.[ 110 ] Another study of a different health insurance programme in Colombia found an opposite effect.[ 48 ]
This review includes a large variety of study designs and indicators for assessing the multiple potential impacts of health insurance, making it hard to directly compare and aggregate findings. For those studies that used a control group, the use of self-selected controls in many cases creates potential bias. Studies of the effect of CBHI are often better at establishing the counterfactual by allowing the use of randomisation in a small area, whereas government schemes or social health insurance covering larger populations have limited opportunity to use randomisation. Non-randomised studies are more susceptible to confounding factors unobserved by the analysts. For a better understanding of the links between health insurance and relevant outcomes, there is also a need to go beyond quantitative evidence alone and combine the quantitative findings with qualitative insights. This is particularly important when trying to interpret some of the counterintuitive results encountered in some studies.
The impact of different health insurance schemes in many countries on utilisation generally shows a positive effect. This is aligned with the supply-demand theory in whichhealth insurance decreases the price of health care services resulting in increased demand. It is difficult to draw an overall conclusion about the impact of health insurance on financial protection, most likely because of differences in health insurance programmes. The impact of health insurance on health status suggests a promising positive effect, but more studies from different countries is required.
The interest in achieving UHC via publicly funded health insurance is likely to increase even further in the coming years, and it is one of the United Nation’s Sustainable Development Goals (SDGs) for 2030[ 111 ]. As public health insurance is still being widely implemented in many LMICs, the findings from this review should be of interest to health experts and policy-makers at the national and the international level.
The authors received no specific funding for this work.
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Advancing social justice, promoting decent work
Ilo is a specialized agency of the united nations, impact insurance research paper #10: health insurance participation.
Research Paper #10 describes the main findings of a field experiment offering health insurance to tea farmers in Kenya. Insurance demand increases substantially when price discount is offered, but contrary to expectations, the education campaign did not have any significant effect on insurance demand despite the evidence of improved insurance literacy among participants. Also referral incentives had a negative influence on insurance demand which suggests that trust is an important element to promote insurance uptake. Additionally, reduction in price impacts significantly insurance demand, a lesson to be considered when pricing microinsurance products.
- Open access
- Published: 13 July 2017
Knowledge and understanding of health insurance: challenges and remedies
- Andrew J. Barnes 1 &
- Yaniv Hanoch 2
Israel Journal of Health Policy Research volume 6 , Article number: 40 ( 2017 ) Cite this article
The Original Article was published on 07 March 2017
As coverage is expanded in health systems that rely on consumers to choose health insurance plans that best meet their needs, interest in whether consumers possess sufficient understanding of health insurance to make good coverage decisions is growing. The recent IJHPR article by Green and colleagues—examining understanding of supplementary health insurance (SHI) among Israeli consumers—provides an important and timely answer to the above question. Indeed, their study addresses similar problems to the ones identified in the US health care market, with two notable findings. First, they show that overall—regardless of demographic variables—there are low levels of knowledge about SHI, which the literature has come to refer to more broadly as “health insurance literacy.” Second, they find a significant disparity in health insurance literacy between different SES groups, where Jews were significantly more knowledgeable about SHI compared to their Arab counterparts.
The authors’ findings are consistent with a growing body of literature from the U.S. and elsewhere, including our own, presenting evidence that consumers struggle with understanding and using health insurance. Studies in the U.S. have also found that difficulties are generally more acute for populations considered the most vulnerable and consequently most in need of adequate and affordable health insurance coverage.
The authors’ findings call attention to the need to tailor communication strategies aimed at mitigating health insurance literacy and, ultimately, access and outcomes disparities among vulnerable populations in Israel and elsewhere. It also raises the importance of creating insurance choice environments in health systems relying on consumers to make coverage decisions that facilitate the decision process by using “choice architecture” to, among other things, simplify plan information and highlight meaningful differences between coverage options.
A major policy drama is taking place in the US where the government is in the process of deciding whether to repeal and replace the ACA (better known as Obamacare). The program, among other things, offers health coverage for millions of Americans who have never held or purchased health insurance in their lives and is the reason for the historically high rates of insurance coverage in the US currently. Despite these successes in coverage expansion, many consumers—especially minorities and low SES individuals—have limited knowledge about the nature and terminology of health insurance [ 1 ], with growing indication that consumers are having difficulty in purchasing insurance plans that offer them adequate risk protection [ 2 ]. Obamacare, however, is not unique in facing this problem. An earlier US coverage expansion, known as Medicare part D, which offers standalone prescription drug coverage to (mainly) older adults, has exposed similar patterns. Indeed, empirical studies and secondary data analysis have repeatedly shown that beneficiaries do not have full command of the program and often, for example, focus on premiums rather than total expected cost leading to higher overall costs [ 3 ].
Much of our knowledge about consumers’ understanding of and decisions about health insurance is based on studies from the US health care market. One might wonder, therefore, if these findings are solely endemic to the US, or whether they can be generalized to other countries and populations.
The paper by Green and colleagues—examining understanding of supplementary health insurance (SHI) among Israeli consumers—provides important and timely information about the experience of consumers outside the US [ 4 ]. Indeed, their study addresses similar problems to the ones identified in the US health care market, with two notable findings. First, they show that overall—regardless of demographic variables—there are low levels of knowledge about SHI, which the literature has come to refer to more broadly as “health insurance literacy.” Indeed, Green et al. report that less than 50% of the participants could answer questions correctly about the various services covered by SHI (see [ 4 ], Table 2), and about a third of the sample indicated that they have never even examined what coverage the SHI offers. Their findings, it might be argued, are slightly more alarming than those typically reported among US participants, as the coverage rates of SHI among participants is rather high (about 77% of the sample). That is, participants’ poor knowledge about SHI did not stem from lack of experience, but from variables that are yet to be investigated.
Green et al.’s second main result shows the existence of a significant disparity in health insurance literacy between different SES groups, where Jews were more knowledgeable about SHI compared to their Arab counterparts. The gap persisted even after controlling for sociodemographic descriptors that might confound the relationship between ethnicity and health insurance literacy (e.g., education, socioeconomic status, SHI ownership), suggesting a critical disconnect between Israelis' perceptions of what services SHI covers and what services SHI actually covers.
The authors’ findings have empirical support from a growing body of literature, including our own, presenting consistent evidence that consumers struggle with understanding and using health insurance. Studies in the US have found that these difficulties are generally more acute for populations considered the most vulnerable and consequently most in need of adequate and affordable health insurance coverage. Health systems, like Israel’s and many others, which rely heavily on consumers’ ability to choose and use coverage, should be concerned that the populace has sufficient levels of health insurance literacy to understand the structure of health benefits and basic cost-sharing concepts well enough to make effective choices [ 5 ].
To understand the pervasive lack of health insurance literacy among many populations and the implications of this deficit on consumers’ ability to choose and use health insurance, consider again the US, where most of our research on this topic has been conducted. More than half of the US adult population lacks the facility with mathematics essential to understand health insurance information [ 6 ]. Footnote 1 Previous studies have shown that insured people do not understand key insurance terms, risk, and the likely out-of-pocket costs when they experience an illness, nor do they understand what is and is not covered by their insurance plans [ 7 , 8 , 9 ].
Limited understanding of health insurance is particularly acute among low-income and otherwise disadvantaged populations [ 2 , 8 , 9 , 10 ]. Several studies demonstrate that poor health insurance literacy results in people making unambiguously bad choices for themselves, leading to excess medical spending, with older and lower income individuals worst off [ 11 , 12 ]. Similarly, Green et al. emphasize that Arab populations in Israel, whom they showed to have lower health insurance literacy, tend to be in poorer health and have lower income, less education, and worse access to health care when compared to Jews living in Israel, contributing to the “inequality in the (Israeli) health system” [ 4 ]. Importantly, Green et al.’s results provide preliminary evidence supporting ethnicity as a unique marker for low health insurance literacy among Israelis even after controlling for socioeconomic status, education, and access to health care.
While the work of Green et al. makes an important contribution to the literature, the next phase of this line of inquiry should, we believe, focus on addressing low levels of health insurance literacy generally and among more vulnerable populations specifically. Needless to say, no magical formula exists that can easily solve this complex problem. However, our own research and that of others has highlighted three possible avenues. First, policymakers and supplementary health insurance funds should ensure that SHI information (e.g., leaflets) is presented and communicated in a range of languages and in a simplified way (e.g., avoiding technical terms), such that individuals from all sections of the population can read and understand it. SHI funds, for example, can imitate the way health care providers have tailored information to effectively communicate with patients and developed a shared decision-making model [ 13 ]. Second, SHI funds can improve the SHI decision environment. Better known as choice architecture, a growing body of research—largely inspired by the emerging field of behavioral economics—has devoted much effort and time to examining ways to improve the decision environment in which consumers operate. Some options to do so that payers can utilize include: reduce the number of SHI options consumers face, present choices in order of price and/or quality, create defaults, use symbolic representation, and standardize coverage options [ 14 ]. Third, SHI funds can coordinate with Arab community groups to target outreach and tailor SHI enrollment and education campaigns to improve how these populations understand and use health care coverage. These are some promising mechanisms that have been identified previously. Future research would need to evaluate their feasibility and appropriateness to the SHI market in Israel, and possibly develop novel ways to address the problem.
When there is a mismatch between health care needs and plan choices resulting from poor health insurance literacy, consumers may not have adequate risk protection to cover their expected health care needs or they may purchase unnecessary coverage. Green and colleagues’ important findings add to a growing literature on health insurance literacy, most of which concludes that consumers do not understand key health insurance terms and have difficulty aligning what they want in an insurance plan with what they choose [ 15 ]. The authors’ findings call attention to the need to tailor communication strategies aimed at mitigating health insurance literacy and, ultimately, access and outcomes disparities among vulnerable populations in Israel and elsewhere. It also highlights the importance of creating choice environments that facilitate the decision process, referred to as “choice architecture,” in health systems relying on consumers to make coverage decisions. Indeed, our own work has revealed that participants with both high and low health insurance literacy benefit from simplifying coverage choices by equal amounts. However the magnitude of this effect represented a larger relative increase among participants with lower health insurance literacy given the disadvantage with which these participants came to the coverage choice environment [ 16 ].
Numeracy and literacy levels among Israeli adults are below OECD average (see http://www.oecd.org/skills/piaac/Skills-Matter-Israel.pdf ). As such, there is little reason to believe that the results from the US would be dramatically different.
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Barnes, A.J., Hanoch, Y. Knowledge and understanding of health insurance: challenges and remedies. Isr J Health Policy Res 6 , 40 (2017). https://doi.org/10.1186/s13584-017-0163-2
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DOI : https://doi.org/10.1186/s13584-017-0163-2
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Health insurance sector in India: an analysis of its performance
Vilakshan - XIMB Journal of Management
ISSN : 0973-1954
Article publication date: 30 November 2020
Issue publication date: 16 December 2020
Health insurance is one of the major contributors of growth of general insurance industry in India. It alone accounts for around 29% of total general insurance premium income earned in India. The growth of this sector is important from the perspective of overall growth of general insurance Industry. At the same time, problems in this sector are also many which are affecting its performance.
The paper provides an understanding on performance of health insurance sector in India. This study attempts to find out how much claims and commission and management expenses it has to incur to earn certain amount of premium. Methodology used for the study is regression analysis to establish relationship between dependent variable (Profit/Loss) and independent variable (Health Insurance Premium earned).
Findings of the study indicate that there is significant relationship between earned premium and underwriting loss. There has been increase of premium earnings which instead of increasing profit for the sector in fact has increased underwriting loss over the years. The earnings of the sector is growing at compounded annual growth rate of 27% still it is unable to earn underwriting profit.
This study is self-driven based on secondary data obtained from insurance regulatory and development authority site.
- Health insurance premium
- Management expenses
- Insurance regulatory and development authority
- Underwriting loss
- Compound annual growth rate
Dutta, M.M. (2020), "Health insurance sector in India: an analysis of its performance", Vilakshan - XIMB Journal of Management , Vol. 17 No. 1/2, pp. 97-109. https://doi.org/10.1108/XJM-07-2020-0021
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Copyright © 2020, Madan Mohan Dutta.
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1.1 meaning of insurance.
Insurance is a contract between two parties where by one party agrees to undertake the risk of the other in exchange for consideration known as premium and promises to indemnify the party on happening of an uncertain event. The great advantage of insurance is that it spreads the risk of a few people over a large group of people exposed to risk of similar type.
Insurance has been identified as a sunrise sector by the financial planners of India. The insurance industry has lot of potential to grow, penetrate and service the masses of India. Insurance is all about protection. An insured needs two types of protection life and non-life. General insurance industry deals with non-life protection of the insured of which health insurance is a part.
1.2 Meaning of health insurance
Health insurance is a part of general insurance which contributes about 29% of premium amongst all other sectors of general insurance. But problems in this sector are many which is the driving force behind this study. This study will help the insurance companies to understand their performance and the quantum of losses that this sector is making over the years.
A plan that covers or shares the expenses associated with health care can be described as health insurance. These plans fall into commercial health insurance, which is provided by government, private and stand-alone health insurance companies.
Health insurance in India typically pays for only inpatient hospitalization and for treatment at hospitals in India. Outpatient services are not payable under health policies in India. The first health policy in India was Mediclaim Policy. In 2000, the Government of India liberalized insurance and allowed private players into the insurance sector. The advent of private insurers in India saw the introduction of many innovative products like family floater plans, critical illness plans, hospital cash and top-up policies.
Health insurance in India is an emerging insurance sector after life and automobile insurance sector. Rise in middle class, higher hospitalization cost, expensive health care, digitization and increase in awareness level are some important drivers for the growth of health insurance market in India.
Lifestyle diseases are on the rise. A sedentary lifestyle has pervaded our being. There is lower physical labour today than earlier and there is no reason why this would not be the trend going forward. The implication is the advent of lifestyle chronic diseases such as cardiac problems and diabetes.
In the context of the Indian health insurance industry, one could look at it both ways. Mired by low penetration and negative consumer perception about its utility are affecting the prospect of this industry. The flipside though is that we have hardly scratched the surface of the opportunity that lies in the future. It is as if the glass is half full. Much remains to be conquered and even more remains to be accomplished.
Health insurance companies needs to be optimistic and have courage to bring in innovation in the areas of product, services and distribution system. Bring it to the fold as the safety net that smartly covers and craft a health insurance plan befitting the need of the customers.
1.3 Background of health insurance sector in India
India’s tryst with health insurance programme goes back to the late 1940s and early 1950s when the civil servants (Central Government Health Scheme) and formal sector workers (Employees’ State Insurance Scheme) were enrolled into a contributory but heavily subsidized health insurance programmes. As a consequence of liberalization of the economy since the early 1990s, the government opened up private sector (including health insurance) in 1999. This development threw open the possibility for higher income groups to access quality care from private tertiary care facilities. However, India in the past five years (since 2007) has witnessed a plethora of new initiatives, both by the central government and a host of state governments also entering the bandwagon of health insurance. One of the reasons for initiating such programs may be traced to the commitment of the governments in India to scale up public spending in health care.
1.4 The need for health insurance in India
1.4.1 lifestyles have changed..
Indians today suffer from high levels of stress. Long hours at work, little exercise, disregard for a healthy balanced diet and a consequent dependence on junk food have weakened our immune systems and put us at an increased risk of contracting illnesses.
1.4.2 Rare non-communicable diseases are now common.
Obesity, high blood pressure, strokes and heart attacks, which were earlier considered rare, now affect an increasing number of urban Indians.
1.4.3 Medical care is unbelievably expensive.
Medical breakthroughs have resulted in cures for dreaded diseases. These cures however are available only to a select few. This is because of high operating and treatment expenses.
1.4.4 Indirect costs add to the financial burden.
Indirect sources of expense like travel, boarding and lodging, and even temporary loss of income account for as much as 35% of the overall cost of treatment. These facts are overlooked when planning for medical expenses.
1.4.5 Incomplete financial planning.
Most of us have insured our home, vehicle, child’s education and even our retirement years. Ironically however we have not insured our health. We ignore the fact that illnesses strike without warning and seriously impact our finances and eat into our savings in the absence of a good health insurance or medical insurance plan.
1.5 Classification of health insurance plans in India
Health insurance plans in India today can be broadly classified into the following categories:
Hospitalization plans are indemnity plans that pay cost of hospitalization and medical costs of the insured subject to the sum insured. There is another type of hospitalization policy called a top-up policy . Top-up policies have a high deductible typically set a level of existing cover.
1.5.2 Family floater health insurance.
Family health insurance plan covers entire family in one health insurance plan. It works under assumption that not all member of a family will suffer from illness in one time.
1.5.3 Pre-existing disease cover plans.
It offers covers against disease that policyholder had before buying health policy. Pre-existing disease cover plans offers cover against pre-existing disease, e.g. diabetes, kidney failure and many more. After waiting for two to four years, it gives covers to the insured.
1.5.4 Senior citizen health insurance.
This type of health insurance plan is for older people in the family. It provides covers and protection from health issues during old age.
1.5.5 Maternity Health insurance.
Maternity health insurance ensures coverage for maternity and other additional expenses.
1.5.6 Hospital daily cash benefit plans.
Daily cash benefits are a defined benefit policy that pays a defined sum of money for every day of hospitalization.
1.5.7 Critical illness plans.
These are benefit-based policies which pay a lump sum amount on certain critical illnesses, e.g. heart attack, cancer and stroke.
1.5.8 Disease-specific special plans.
Some companies offer specially designed disease-specific plans such as Dengue Care and Corona Kavach policy.
1.6 Strength, weakness, opportunity and threat analysis of health insurance sector (SWOT analysis)
The strengths, weaknesses, opportunities and threats (SWOT) is a study undertaken to identify internal strengths and weaknesses as well as external opportunities and threats of the health insurance sector.
The growth trend of the health insurance sector is likely to be high due to rise in per capita income and emerging middle-income group in India. New products are being launched in this sector by different insurance companies which will help to satisfy customers need. Customers will be hugely benefited when cash less facility will be provided to all across the country by all the insurance companies.
The financial condition of this sector is weak due to low investment in this sector. The public sector insurance companies are still dominating this industry due to their greater infrastructure facilities. This sector is prone to high claim ratio and many false claims are also made.
The possibility of future growth of this sector is high, as penetration in the rural sector is low. The improvement of technology and the use of internet facility are helping this sector to grow in magnitude and move towards environment-friendly paperless regime.
The biggest threat of this sector lies in the change in the government regulations. The profitability of this sector is affected due to increasing expenses and claims. The economic slowdown and recession in the economy can affect growth of this sector adversely. The increasing losses and need for insurance might reach a point of no return where insurance companies may be compelled to decline an insurance policy.
1.7 Political economic socio cultural and technological analysis of health insurance sector (PEST analysis)
This analysis describes a framework of macro-environmental factors used as strategic tool for understanding business position, growth potential and direction for operations.
1.7.1 Political factors.
Service tax on premium on insurance policies is being increased by the government for past few years during budget. Government monopoly in this sector came to an end after insurance companies were opened up for private participation in the year 2000. Foreign players were allowed to enter into joint venture with their Indian counterpart with 26% holding and which was further increased to 49% in the year 2015.
1.7.2 Economic factors.
The gross savings of people in India have increased significantly thereby encouraging people to buy insurance policy to cover their risks. Insurance companies are fast becoming prominent players in the security market. As these companies have huge disposable income which they are investing in the security market.
1.7.3 Socio-cultural factors.
Increase in insurance knowledge is helping people to increase their awareness about the risk to be covered through insurance. Change in lifestyle is leading to increase in risk thereby giving an opportunity to insurance companies to innovate newer products. Societal benefit is derived by transfer of risk through insurance due to improved socio-cultural environment.
1.7.4 Technological factors.
Insurance companies deals in large database and maintaining it by the application of latest technology is huge gain for this sector. Technological advancement has helped insurance companies to sale their products through their electronic portals. This has made their task of providing service to the customers easier and faster.
2. Review of literature
After opening up of the insurance industry health insurance sector has become significant both from economic and social point of view and researchers have explored and probed these aspects.
Ellis et al. (2000) reviewed a variety of health insurance systems in India. It was revealed that there is a need for a competitive environment which can only happen with the opening up of the insurance sector. Aubu (2014) conducted a comparative study on public and private companies towards marketing of health insurance policies. Study revealed that private sector services evoked better response than that of public sector because of new strategies and technologies adopted by them. Nair (2019) has made a comparative study of the satisfaction level of health insurance claimants of public and private sector general insurance companies. It was revealed that majority of the respondents had claim of reimbursement nature through third party administrator. Satisfaction with respect to settlement of claim was found relatively higher for public sector than private sector. Devadasan et al. (2004) studied community health insurance to be an important intermediate step in the evolution of an equitable health financing mechanism in Europe and Japan. It was concluded that community health insurance programmes in India offer valuable lessons for its policy makers. Kumar (2009) examined the role of insurance in financing health care in India. It was found that insurance can be an important means of mobilizing resources, providing risk protection and health insurance facilities. But for this to happen, it will require systemic reforms of this sector from the end of the Government of India. Dror et al. (2006) studied about willingness among rural and poor persons in India to pay for their health insurance. Study revealed that insured persons were more willing to pay for their insurance than the uninsured persons. Jayaprakash (2007) examined to understand the hurdles preventing the people to purchase health insurance policies in the country and methods to reduce claims ratio in this sector. Yadav and Sudhakar (2017) studied personal factors influencing purchase decision of health insurance policies in India. It was found that factors such as awareness, tax benefit, financial security and risk coverage has significant influence on purchase decision of health insurance policy holders. Thomas (2017) examined health insurance in India from the perspective of consumer insights. It was found that consumers consider various aspects before choosing a health insurer like presence of a good hospital network, policy coverage and firm with wide product choice and responsive employees. Savita (2014) studied the reason for the decline of membership of micro health insurance in Karnataka. Major reason for this decline was lack of money, lack of clarity on the scheme and intra house-hold factors. However designing the scheme according to the need of the customer is the main challenge of the micro insurance sector. Shah (2017) analysed health insurance sector post liberalization in India. It was found that significant relationship exists between premiums collected and claims paid and demographic variables impacted policy holding status of the respondents. Binny and Gupta (2017) examined opportunities and challenges of health insurance in India. These opportunities are facilitating market players to expand their business and competitiveness in the market. But there are some structural problems faced by the companies such as high claim ratio and changing need of the customers which entails companies to innovate products for the satisfaction of the customers. Chatterjee et al. (2018) have studied health insurance sector in India. The premise of this paper was to study the current situation of the health-care insurance industry in India. It was observed that India is focusing more on short-term care of its citizens and must move from short-term to long-term care. Gambhir et al. (2019) studied out-patient coverage of private sector insurance in India. It was revealed that the share of the private health insurance companies has increased considerably, despite of the fact that health insurance is not a good deal. Chauhan (2019) examined medical underwriting and rating modalities in health insurance sector. It was revealed that while underwriting a health policy one has to keep in mind the various aspects of insured including lifestyle, occupation, health condition and habits. There have been substantial studies on health insurance done in India and abroad. But there has not been any work on performance of health insurance sector based on underwriting profit or loss.
3. Research gap
After extensive review of literature it is understood that there has not been substantial study on the performance of health insurance sector taking underwriting profit or loss into consideration. In spite of high rate of growth of earned premium, this sector is unable to make underwriting profit. This is mainly because growth of premium is more than compensated by claims incurred and commission and other expenses paid. Thereby leading to growth of underwriting loss over the years across the different insurance companies covered under both public and private sector. This unique feature of negative performance of this sector has not been studied so far in India.
review health insurance scenario in India; and
study the performance of health insurance sector in India with respect to underwriting profit or loss by the application of regression analysis.
5. Research methodology
The study is based on secondary data sourced from the annual reports of Insurance Regulatory Development Authority (IRDA), various journals, research articles and websites. An attempt has been made to evaluate the performance of the health insurance sector in India. Appropriate research tools have been used as per the need and type of the study. The information so collected has been classified, tabulated and analysed as per the objectives of the study.
The data is based on a time period of 12 years ranging from 2006–2007 to 2018–2019.
Secondary data analysis has been done using regression of the form: Y = a + b X
The research has used SPSS statistics software package for carrying out regression and for the various graphs Microsoft Excel software has been used.
5.1 The problem statement
It is taken to be a general assumption that whenever the premium increases the profit also increases. This determines that profits are actually dependent on the premium income. Hence, whenever the premium tends to increase, the profit made also supposed to increase.
The aim of the study is to find out whether the underwriting profit of the health insurance sector is increasing or there is an underwriting loss.
The problem statement is resolved by applying regression analysis between the premium earned and underwriting profit or loss incurred. It is assumed that if the underwriting profit increases along with the premium received, then the pattern forms a normal distribution and alternate hypothesis can be accepted and if this pattern of dependability is not found then the null hypothesis will be accepted stating that there is no relation between the premium and the underwriting loss or the underwriting profit by the sector. But what is happening in this sector is the increase in premium is leading to increase in underwriting loss. So premium is negatively impacting underwriting profit which is astonishing thing to happen and is the crux of the problem of this sector.
5.1.1 Underwriting profit/loss = net premium earned – (claim settled + commission and management expenses incurred).
Underwriting profit is a term used in the insurance industry to indicate earned premium remaining after claims have been settled and commission and administrative expenses have been paid. It excludes income from investment earned on premium held by the company. It is the profit generated by the insurance company in the normal course of its business.
5.2 Data analysis
Table 1 shows that health insurance premium increased from Rs.1910 crores in 2006–2007 to Rs. 33011 crores in 2018–2019. But claims incurred together with commission and management expenses have grown from Rs. 3349 crores to Rs. 40076 crores during the same period. So the claims and management expenses incurred together is more than the health insurance premium earned in all the years of our study thereby leading to underwriting loss.
Claim incurred shown above is the outcome of the risk covered against which premium is received and commission and management expenses are incurred to obtain contract of insurance. Both these expenses are important for insurance companies to generate new business as stiff competition exists in this sector since it was opened up in the year 2000.
Figure 1 depicts the relationship between health insurance premium earned and claims and management expenses incurred by the insurance companies of the health insurance sector for the period 2006–2007 to 2018–2019.
Bar chart between premiums earned and claims and management expenses incurred show that claims and management expenses together is higher than premium earned in all the years of the study thereby leading to losses. Claims, commission and management expenses are important factors leading to the sale of insurance policies thereby earning revenue for the insurance companies in the form of premium. But proper management of claims and commission and management expenses will help this sector to improve its performance.
Table 2 provides insight into the performance of health insurance sector in India. The growth of health insurance in India has been from Rs.1909 crores for the financial year 2006–2007 to Rs. 33011crores for the financial year 2018–2019. The growth percentage is 1629% i.e. growing at an average rate of 135% per annum. Compounded Annual Growth Rate (CAGR) is working out to be 27%.
From the same table, it can be inferred that health insurance sector is making underwriting loss in all the financial years. There is no specific trend can be seen, it has increased in some years and decreased in some other years. Here underwriting loss is calculated by deducting claims and commission and management expenses incurred from health insurance premium earned during these periods.
With every unit of increase in premium income the claims incurred together with commission and management expenses paid increased more than a unit. Thereby up setting the bottom line. So instead of earning profit due to better business through higher premium income, it has incurred losses.
Underwriting principles needs to be streamlined so that proper scrutiny of each policy is carried out so that performance of this sector improves.
It is seen from Figure 2 that there is stiff rise in premium earned over the years but claims and commission and management expenses incurred have also grown equally and together surpassed earned premium. So the net impact resulted in loss to this sector which can also be seen in the figure. It is also seen that loss is increasing over the years. So, increase in earnings of revenue in the form of premium is leading to increase in losses in this sector which is normally not seen in any other sectors.
But a time will come when commission and management expenses will stabilize through market forces to minimize underwriting losses. On the other hand, it will also require proper management of claims so that health insurance sector can come of this unprofitable period.
5.3 Interpretation of regression analysis
5.3.1 regression model..
Where Y = Dependent variable
X = Independent variablea = Intercept of the lineb = Slope of the line
5.3.2 Regression fit.
Here, Y is dependent variable (Underwriting Profit or Loss) which is to be predicted, X is the known independent variable (Health Insurance Premium earned) on which predictions are to be based and a and b are parameters, the value of which are to be determined ( Table 3 ). Y = − 1028.737 − 0.226 X
5.3.3 Predictive ability of the model.
The value of R 2 = 0.866 which explains 86.6% relationship between health insurance premium earned and loss made by this sector ( Table 4 ). In other words, 13.4% of the total variation of the relationship has remained unexplained.
4.1 Regression coefficients ( Table 5 ).
H1.1 : β = 0 (No influence of Health Insurance Premium earned on Underwriting Profit or Loss made)
188.8.131.52 Alternative hypothesis.
H1.2 : β ≠ 0 (Health Insurance Premium earned influences underwriting Profit or Loss made by this sector)
The computed p -value at 95% confidence level is 0.000 which is less than 0.05. This is the confidence with which the alternative hypothesis is accepted and the null hypothesis is rejected. Thus regression equation shows that there is influence of health insurance premium earned on loss incurred by this sector.
The outcome obtained in this analysis is not what happens normally in the industry. With the increase of revenue income in the form of premium, it may lead to either profit or loss. But what is happening surprisingly here is that increase of revenue income is leading to increase of losses. So growth of premium income instead of influencing profit is actually influencing growth of losses.
The finding from the analysis is listed below:
The average growth of net premium for the health insurance has been around 135% per annum even then this sector is unable to earn underwriting profit.
The CAGR works out to around 27%. CAGR of 27% for insurance sector is considered to be very good rate of growth by any standard.
Along with high growth of premium, claims and commission and management expenses incurred in this sector have also grown substantially and together it surpassed in all the years of the study.
Thus, growth of claims and commission and management expenses incurred has more than compensated high rate of growth of health insurance premium earned. This resulted into underwriting loss that this sector is consistently making.
Astonishing findings has been higher rate of increase of premium earnings leading to higher rate of underwriting loss incurred over the years. Even though the sector is showing promise in terms of its revenue collection, but it is not enough to earn underwriting profit.
COVID 19 outbreak in India has led to a spike in health-care costs in the country. So, upward revision of premium charges must be considered to see bottom line improvement in this sector.
Immediate investigation of the claim is required. This will enable the insurers to curb unfair practice and dishonest means of making a claim which is rampant in this sector.
Health insurance market is not able to attract younger generation of the society. So entry age-based pricing might attract this group of customers. An individual insured at the age 30 and after 10 years of continuous coverage the premium will be less than the other individual buying a policy at the age of 40 for the first time.
6.3 Limitations and scope of future studies
The analysis of performance of health insurance sector in India taking underwriting profit into consideration is the only study of its kind in this sector. As a result, adequate literature on the subject was not available.
Health insurance and health care are part of medical care industry and are inter dependent with each other. So performance of health insurance sector can be better understood by taking health-care industry into consideration which is beyond the scope of the study.
This sector is consistently incurring losses. So, new ideas need to be incorporated to reduce losses if not making profits.
Opportunity of the insurance companies in this sector lies in establishing innovative product, services and distribution channels. So, continuous modification by the application of research is required to be undertaken.
Health insurance sector will take a massive hit, as tax benefit is going to be optional from this financial year. This can be a subject of study for the future.
This sector is prone to claims and its bottom line is always under tremendous pressure. In recent times, IRDA has taken bold step by increasing the premium rate of health insurance products. This will help in the growth of this sector.
With better technological expertise coming in from the foreign partners and involvement by the IRDA the health insurance sector in India must turn around and start to earn profit.
The COVID-19 pandemic is a challenge for the health insurance industry on various fronts at the same time it provides an opportunity to the insurers to fetch in new customers.
The main reason for high commission and management expense being cut-throat competition brought in after opening up of the insurance sector in the year 2000. So, new companies are offering higher incentives to the agents and brokers to penetrate into the market. This trend needs to be arrested as indirectly it is affecting profitability of this sector.
The study will richly contribute to the existing literature and help insurance companies to know about their performance and take necessary measures to rectify the situation.
Chart on health insurance premium earned and claims and management expenses paid
Chart on performance of health insurance sector in India
Data showing health insurance premium earned and claims and management expenses paid
. Dependent variable: Underwriting profit or loss;
. Predictors: (Constant), Health insurance premium earned
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Original research article, perceptions of the benefits of the basic medical insurance system among the insured: a mixed methods research of a northern city in china.
- 1 Department of Social Medicine, School of Health Management, Harbin Medical University, Harbin, China
- 2 School of Public Health, Harbin Medical University, Harbin, China
- 3 Research Center of Health Policy and Management, School of Health Management, Harbin Medical University, Harbin, China
Background: The perceptions of the benefits of the basic medical insurance system among the insured not only reflect the system's performance but also the public's basic medical insurance policy literacy, valuable information for countries that have entered the stage of deepening reform. This study aims to examine the factors that affect the perceptions of the benefits of the basic medical insurance system in China, diagnose the key problems, and propose corresponding measures for improvement.
Methods: A mixed method design was used. Data for the quantitative study were obtained from a cross-sectional questionnaire survey ( n = 1,045) of residents of Harbin who had enrolled for basic medical insurance system. A quota sampling method was further adopted. A multivariate logistic regression model was then employed to identify the factors influencing the perceptions of the benefits of the basic medical insurance system, followed by semi-structured interviews with 30 conveniently selected key informants. Interpretative phenomenological analysis was used to analyze the interview data.
Results: Approximately 44% of insured persons reported low perceptions of benefits. The logistic regression model showed that low perceptions of the benefits of the basic medical insurance system was positively correlated with the experience of daily drug purchases (OR = 1.967), perceptions of recognition with basic medical insurance system (OR = 1.948), perceptions of the financial burden of participation costs (OR = 1.887), perceptions of the convenience of using basic medical insurance for medical treatment (OR = 1.770), perceptions of the financial burden of daily drug purchases costs (OR = 1.721), perceptions of the financial burden of hospitalization costs (OR = 1.570), and type of basic medical insurance system (OR = 1.456). The results of the qualitative analysis showed that the key problem areas of perceptions of the benefits of the basic medical insurance system were: (I) system design of basic medical insurance; (II) intuitive cognition of the insured; (III) rational cognition of the insured; and (IV) the system environment.
Conclusions: Improving the perceptions of the benefits of the basic medical insurance system of the insured requires joint efforts in improving system design and implementation, exploring effective publicity methods of basic medical insurance system information, supporting public policy literacy, and promoting the health system environment.
Medical insurance systems are often used to improve the accessibility and equity of health services and to protect their populations from medical poverty ( 1 ). Globally, many countries regard it as an important part of the social security system and promote universal coverage ( 2 , 3 ). The insured are not only the target group of the system, but also the actual users and final evaluators. Their perceptions and evaluations of benefits are a comprehensive reflection of many factors, such as objective system design and implementation ( 4 ), subjective expectation ( 5 ), cognition and emotion as well as practical needs ( 6 , 7 ). Making the public truly experience the benefits is an important goal of medical insurance reform. It is important and necessary to systematically examine the current situation, the influencing factors and key problems of the perceptions of benefits among the insured.
China has made significant progress in the construction and development of basic medical insurance systems (BMIS) in many aspects. Initially, the country provided economic security for more than 1.3 billion people through the establishment of three basic systems: namely, the urban employee basic medical insurance (UEBMI) for urban employees, the urban resident basic medical insurance (URBMI) for unemployed urban residents, and the new rural cooperative medical scheme (NCMS) for rural residents ( 8 ). After basically realizing universal coverage (>95% of the population), China integrated URBMI and NCMS into the urban and rural resident basic medical insurance (URRBMI) scheme ( 9 ) to improve the fund's risk sharing ability, management efficiency, and institutional fairness. Regarding service coverage, the country has implemented the dynamic adjustment policy of a drug catalog and has gradually optimized the types and quantity of reimbursable drugs ( 10 ). Moreover, to cooperate with the hierarchical medical system, the government has formulated the differentiated reimbursement policy of BMIS to reasonably guide patients to different medical institutions, which increases the access to and efficiency of medical services ( 11 ). Regarding health service cost coverage, the country has conducted a reform of the payment method of BMIS to control the increase in medical expenses ( 12 ). To improve the level of welfare, the Chinese government has increased financial subsidies and is expanding the proportion of reimbursement yearly ( 13 ). Additionally, the government has worked to enhance the support policies of BMIS at the national level, such as by establishing a legal system to ensure the safety of funds ( 14 ). Overall, the Chinese BMIS is working in a people-centered direction and is playing a vital role in improving the level of benefits for the insured.
However, existing evidences suggest that these efforts have not been effective in increasing the subjective perceptions and evaluations of the insured. Jing et al. ( 15 ) conducted a questionnaire survey with 3,231 insured people and found that 51.3% of them had medium or low satisfaction with BMIS. According to Liu et al. ( 16 ), the insured are generally dissatisfied with the quality of BMIS policies. Shan et al. ( 5 ) reached similar conclusions and found that the situation of BMIS is far from the expectations and preferences of the insured. Additionally, many scholars have confirmed this view from different angles. Tao et al. ( 17 ) showed that China's out of pocket expenditures (OOPE) decreased significantly, from 60.13% in 2000 to 35.91% in 2016, but were significantly higher than those of OECD member countries. The number of OOPE per capita continues to increase. Accordingly, the economic pressure of the insured is still serious ( 18 ). Shan et al. ( 5 , 19 ) showed that many insured had low perceptions of the fairness and convenience of BMIS. The insured's perceptions of the key performance of BMIS reform can reflect the shortcomings of the reform and the problems that require key intervention in the future.
Regarding the insured's perceptions of the benefits of the basic medical insurance system (PBBMI), most scholars have explored the system design problems of the current BMIS via quantitative research methods, such as the poor protection effect of medical insurance on the seriously ill population ( 20 ), the insufficient protection on the fairness of different groups ( 21 ), the poor publicity effect of medical insurance information ( 22 ), and the poor portability of medical insurance ( 5 ). In most related qualitative studies, scholars have studied BMIS by interviewing system administrators or the insured. The administrators mainly focused on the management, financing, population, welfare design, structure, operating procedures, and interaction with health service providers ( 23 , 24 ). The insured mainly focused on the coverage of BMIS and the fairness of services ( 25 – 27 ). Although previous research has made valuable efforts, there are still some deficiencies in the existing literature. First, most scholars have focused on a single dimension of BMIS and lack an evaluation of the perceptions of benefits of comprehensive medical insurance performance in multiple dimensions. Second, subjective cognitive factors of the insured are rarely discussed ( 28 ). Third, few studies have used mixed-methods research, which can overcome certain limitations of quantitative and qualitative research and allow researchers to fully capture the complexity of measurement and obtain reliable findings ( 29 ). Fourth, previous scholars often equate satisfaction with perceptions of benefits ( 5 ). However, BMIS satisfaction has evolved from commercial customer satisfaction ( 30 ), which is more biased toward evaluating BMIS as a private product similar to ordinary goods, and therefore hardly reflects the public benefits of BMIS. Based on the foundation and shortcomings of previous scholars, this study proposes the concept of PBBMI. Participants with different health status, health expectations, and socio-economic characteristics have overall perceptions of benefits of BMIS related to the needs of health services such as medical treatment, prevention, health care, and rehabilitation in the process of improving their own health. According to the key performance areas of BMIS, the composition of PBBMI should include the perceptions of financial burden, convenience, fairness, regulation, utility, awareness, and recognition.
This study aimed to answer the following question: Is the progress of China's BMIS in terms of coverage reflected in the PBBMI of the insured? By aiming to understand participants' overall perspective and by using a mixed-methods design, this study investigated which factors may affect the low PBBMI of the insured from a number of perspectives, such as varied socio-demographic status, health and disease status, recent medical insurance use and affairs-handling experience, and perceptions of key institutional performance indicators. Further, we aimed to determine the key issues in the system performance areas that have the greatest impact on the insured's PBBMI. This study's findings can provide more accurate intervention guidance for countries and regions where the medical insurance system is in development.
2. Materials and methods
2.1. research design.
This study was conducted in Harbin, China. Harbin is the capital of Heilongjiang Province, located in Northeast China, with a population of 9.885 million people. In 2021, the per capita GDP of Harbin was 53,517 yuan, ranking low among all provincial capitals in China. In general, the basic policies and regulations of China's BMIS are formulated by the national government, and local governments can make adjustments according to their own conditions without violating the basic principles. Policies at the national level generally regulate the basic structure of the national basic medical insurance, the reforms that must be implemented, and the minimum security standards. The capable local governments are allowed to make pilot explorations based on complying with the trend of national BMIS reform. However, the regions that made pilot reforms of the system in advance account for a small proportion in China. The arrangement and reform of the BMIS in Harbin, Heilongjiang Province is consistent with most regions in China and essentially mirrors national basic policies and regulations, which can reflect some common phenomena and problems to a certain extent.
A mixed research approach was used in this study. The quantitative study was aimed at identifying the factors that drove the insured's low PBBMI. The qualitative study however, explored the specific problem details of the key performance aspects of BMIS which were significant in the logistic regression results. Based on this, the key problem areas of low PBBMI are summarized. The results of the quantitative and qualitative studies were integrated to suggest improvements.
2.1.1. Quantitative study design
An analytical framework ( Figure 1 ) builds on several theories and research findings. User experience (UE) theory suggests that the products purchased and used should be evaluated based on user experience ( 31 ). Result-oriented performance evaluation theory emphasizes the evaluation of BMIS reform based on the policy performance ( 32 ). Additionally, many researchers have asserted that factors such as “financial burden, convenience, equity, regulation, utility, information awareness, and recognition” can significantly affect the insured's experience of the use of medical insurance ( 11 , 16 , 33 – 37 ). Jiang et al. ( 38 ) suggested that the “health status” of the insured is also a key factor in the perceptions of benefits. Moreover, Sadak et al. and Sanogo et al. ( 39 , 40 ) have shown that the experience of the insured is fundamental to evaluating the perceptions of medical insurance benefits.
Figure 1 . Analytical framework of the PBBMI of the insured.
Based on this analytical framework, a 4-part questionnaire was developed. The first part gathered the demographic and socioeconomic characteristics of the insured. The second part evaluated the PBBMI of the key performance areas of the BMIS of the insured. The third part was aimed at evaluating the health status of the insured. The fourth part further investigated the BMIS use experience of the insured.
2.1.2. Qualitative study design
Based on the seven key performance dimensions of BMIS in the quantitative questionnaire and relevant details in each dimension, an interview outline was developed. We aimed to collect information about major problems perceived by the insured in the key performance areas of BMIS, including the main problems felt about financial burdens (participation costs, outpatient costs, hospital costs, daily drug purchases costs), convenience (enrolment procedures, medical treatment, daily drug purchases, other business works), fairness (participation fees, health services, protection of treatment), regulation (insurance use behavior), utility (health services, primary treatment and two-way referral), awareness (accessibility of information), and recognition (the basic medical insurance system). In addition, we collected demographic and socioeconomic information of the respondents. During the interview, the interviewee was invited to make an overall evaluation of each of the seven dimensions, and was asked to give a detailed description of three problems of the BMIS that they perceived as the least beneficial (including, but not limited, to the above problems).
2.2. Sampling and data collection
2.2.1. quantitative data.
A cross-sectional questionnaire survey was conducted during from December 2020 through February 2021. The survey respondents were selected using a quota sampling method to ensure representativeness. First, we determined balanced quotas for sex, age, education level, employment status, 2-week outpatient visits, hospitalization in the last year, and chronic disease incidence based on population estimates provided by the Heilongjiang Statistical Yearbook ( 41 ), China Health Statistical Yearbook ( 42 ), China National Health and Nutrition Big Data Report ( 43 ), and our past research experience. Three suitable communities in Harbin were then selected for the survey using a convenience sampling method. The sample size was estimated based on the need for logistic regression analysis; thus, it was 10 times more than the number of independent variables. The number of survey respondents was proportional to the size of the selected communities.
The questionnaires were completed in person under the guidance of professional staff who were specially trained before the survey began. As this survey was conducted during the outbreak of COVID-19, the effect that resulted in a decrease in the number of rural population migrating to urban, and the utilization of outpatient and inpatient services by the insured compared to previous periods. A total of 1,063 residents who enrolled for BMIS completed the questionnaire. We excluded incomplete questionnaires and those with logical errors, resulting in a final sample size of 1,045. To ensure the quality of the questionnaire, Cronbach's alpha was used to evaluate the confidence quality level of the data. The Cronbach's alpha of this study was 0.926 (>0.9) ( 44 ), indicating good reliability.
2.2.2. Qualitative data
Interviews were conducted between February and April 2021. The inclusion criteria included insured persons or those whose family members had recently used basic medical insurance. Respondents were selected through a convenience sampling method. Owing to the severe development of COVID-19 at that time, we conducted and recorded telephone interviews based on a semi-structured interview outline after obtaining verbal informed consent from the interviewees; the interviews lasted ~30–60 min. The number of interviewees was determined by the saturation level of the interview content; 30 people were eventually interviewed. The recorded interviews were converted into text for further study.
2.3. Data analysis
2.3.1. quantitative data.
We conducted a multiple logistic regression analysis to determine the influencing factors of the PBBMI of the insured.
184.108.40.206. Dependent variable
The PBBMI of the insured was the dependent variable. Participants responded on a five-point Likert scale (1 = completely disagree, 2 = disagree, 3 = neither disagree nor agree, 4 = agree, 5 = completely agree) to score the following statement: “Overall, I think the current PBBMI is very high.” The score was divided into two categories that is, 0 = low PBBMI (including complete disagree, disagree and neither disagree nor agree) and 1 = high PBBMI (including agree and completely agree) for the purpose of logistic regression modeling.
220.127.116.11. Independent variables
18.104.22.168.1. pbbmi of the key performance areas of bmis.
Participants responded on a five-point Likert scale (1 = completely disagree, 2 = disagree, 3 = neither disagree nor agree, 4 = agree, 5 = completely agree) to score the specific problems in the key performance areas of BMIS (including the perceptions of financial burden, convenience, fairness, regulation, utility, awareness and recognition). Table 1 shows these specific problems. In the regression modeling, the scores were transformed into a dichotomous measurement and coded as: 0 = poor (including complete disagree, disagree and neither disagree nor agree) and 1 = good (including agree and completely agree).
Table 1 . Characteristics of respondents and overall PBBMI ( n = 1,045).
22.214.171.124.2. Health and disease status
Survey respondents were asked to answer the following question: “Out of 100, how would you rate your current health status?” and “Do you have a chronic disease?” In the regression modeling, the median method was used to classify the health self-ratings into 2 categories: 0 = poor ( ≤ 85) and 1 = good (>85). The chronic disease status was divided into 2 categories: 0 = no chronic disease and 1 = with chronic disease.
126.96.36.199.3. Recent basic medical insurance use or affairs handling experience
Survey respondents were asked to answer the following question: “Do you have following experience: outpatient visits within two weeks, hospitalization within one year, preventive care within one year, daily drug purchases and go to the basic medical insurance institutions for business within one year?” In the regression modeling, participants' responses were divided into 2 categories: 0 = no experience and 1 = have experience.
188.8.131.52.4. Control variables
In the statistical analysis, we controlled the confounding effect of the demographic and socioeconomic characteristics (age, gender, marital status, education, etc.) of the survey respondents.
184.108.40.206. Statistical analysis
Data were further analyzed using SPSS 25.0. We initially determined the relationship between the PBBMI and each independent variable using the Chi-square test. We then constructed two regression models, one including all independent variables (Hosmer-Lemeshow test, X 2 = 7.535, p = 0.480) and the other including only those variables that were statistically significant ( p < 0.05) in the Chi-square test (Hosmer-Lemeshow test, X 2 = 6.324, p = 0.611). The second model showed a better fit compared to the first one and slightly different odds ratios (ORs) compared with the first one. Accordingly, we only show the results of the second model.
2.3.2. Qualitative data
Interpretative phenomenological analysis (IPA) was performed with the Interview materials ( 45 ). In the first step, the six researchers were divided into three groups of two persons each; each group conducted interviews with different interviewees. In the second step, the interviewers converted the recordings into text and read them several times to ensure the consistency between the text and the recordings after each interview, and further held regular group meetings to discuss the content of each group's interview. In the case the content of the interview was found to be detached from the research topic or saturated with content, the interview would be adjusted or stopped. In the third step, to ensure consistency in the analysis of textual information, we selected only two researchers to tag and code textual information related to the low PBBMI of the insured. In addition, in this step, we combined the results of quantitative analysis to eliminate irrelevant content in the qualitative data, so as to ensure the consistency of the interpretation of the qualitative results to the quantitative results. In the fourth step, the two researchers repeatedly thought about the connections between the tags and codes of the filtered text content, gathered the connected tags and codes together, summarized and refined them into primary themes, and then held regular group meetings to discuss these themes until a consensus was reached. In the fifth step, the primary themes were summarized and refined to form more representative secondary and tertiary themes.
3.1. Quantitative research phase
3.1.1. characteristics of respondents and the pbbmi of the insured.
The number of female respondents was approximately equal to that of male (50.9%); the majority of respondents were 64 years old or younger (79.4%); married (76.2%); lived in cities (92.4%); had no bachelor degree or higher (83.4%); and were employed (59.5%). More than half of the respondents were UEBMI (52.3%) and a small proportion of respondents had commercial insurance (19.6%).
Overall, ~44% of the insured had low PBBMI. The Chi-square tests showed that the PBBMI was associated with the demographic and socioeconomic characteristics of respondents, the perceptions of key performance areas of BMIS, health status and the use experience of recent basic medical insurance. The respondents who had a lower level of education, were unemployed, and whose average monthly household income is the poorest had lower PBBMI. Those who enrolled for URRMBI and have no commercial insurance are more likely to have a low PBBMI. Apart from the perceptions of regulation, the respondents whose perceptions of the key performance areas of BMIS are poor are more likely to have low PBBMI. Moreover, the respondents whose health self-assessment is good also exhibited lower PBBMI. Those who had no recent use experience of basic medical insurance exhibited lower PBMMI ( Table 1 ).
3.1.2. Logistic regression model
After controlling for confounding factors, the logistic regression model identified seven influencing factors ( p < 0.05) of PBBMI: the experience with daily drug purchases (OR = 1.967), the perceptions of the recognition of BMIS (OR = 1.948), the perceptions of the financial burden of participation costs (OR = 1.887), the perceptions of the convenience of medical treatment (OR = 1.770), the perceptions of the financial burden of daily drug purchases costs (OR = 1.721), the perceptions of the financial burden of hospitalization costs (OR = 1.570) and the type of BMIS (OR = 1.456). Table 2 presents the details.
Table 2 . Logistic regression analysis on the PBBMI.
3.2. Qualitative research phase
Qualitative interviews mainly diagnosed specific problem details of the key performance areas of BMIS, which was significant in the quantitative research. Table 3 shows the main themes that emerged from the analysis. To promote understanding, we have included quotations in Supplementary material 1 .
Table 3 . Main themes.
This exploratory study showed that 44% of the insured exhibited low PBBMI. The quantitative research showed that the PBBMI was associated with the demographic and socioeconomic characteristics of respondents (type of MBIS), recent basic medical insurance use experience (daily drug purchases) and the perceptions of the key performance areas of BMIS (recognition of BMIS, financial burden of participation costs, convenience of medical treatment, financial burden of daily drug purchases costs, financial burden of hospitalization costs). Qualitative research identifies four main areas of the problems about the key performance of BMIS that trigger low PBBMI: (I) the system design of basic medical insurance; (II) the intuitive cognition of the insured; (III) the rational cognition of the insured; and (IV) the system environment.
4.1. Characteristics of the insured with low PBBMI
This study shows that compared with the UEBMI, the insured who enrolled in the URRBMI had lower PBBMI (OR = 1.456). Although the system integration improved the welfare level of the URRBMI ( 38 ), the difference between residents' and employees' contributory capacity means that URRMI and UEBMI in institutional treatment differ significantly, reducing the PBBMI of the insured who enrolled for the URRMI.
Additionally, the results of the chi square test showed that the insured who lacked recent basic medical insurance use experience had lower PBBMI. The public's perceptions of reform and change are lagging, especially in areas where they do not enter frequently ( 22 ). The insured who had not used medical care recently were more likely to be affected by the collective memory of the once extremely insufficient medical security in Heilongjiang ( 46 , 47 ). Among the specific types of usage experiences, daily drug purchase experience was statistically significant in the dimension of recent basic medical insurance use experience (OR = 1.967 compared with no daily drug purchase experience). This was likely because compared with other basic medical insurance use experiences, the insured have higher expectations for inexpensive daily drug purchases. However, the prices in drugstores are now generally higher than in hospitals. This gap between expectation and reality can easily reduce individual's PBBMI.
4.2. Barriers to the PBBMI based on the key performance areas of BMIS
4.2.1. system design of basic medical insurance.
Scientific system design is not only the premise of the smooth operation and effectiveness of the system, but also a significant influencing factor of PBBMI ( 48 ). We found that the problems about key performance areas to which the insured responded were primarily related to the design of the BMIS, including inadequate coverage of guarantee, insufficient attention to the needs of low-income people, and a lack of regulatory measures and support.
220.127.116.11. Inadequate coverage of services and guarantees
The coverage of services and guarantees has been the focus of reform. However, the price of drugs and the lack of reimbursable coverage of drugs and consumables are still issues that have triggered many complaints from the insured.
Of all the respondents, 54% thought that the price of medical drugs was high, which is consistent with the situation of China's drug service market ( 49 ). At the national level, expenditure on drugs has increased gradually ( 50 ). For example, in 2018, China's total drug expenditure was 1,914.89 billion yuan, accounting for 32.39% of the total health expenditure, which is far higher than the average of 17% in OECD countries ( 51 ). In addition, the results of a survey on drug prices in 31 Chinese provinces showed that drug prices in Heilongjiang province, the area we investigated, were generally higher than the national average ( 52 ). He et al. and Zeng et al. believe that this is related to the high price and quantity of drugs used ( 53 , 54 ). China launched a volume procurement policy at the national level in 2018 to reduce drug prices in each province. The average price of 25 and 87 drug varieties selected in the next 2 years decreased by 59 and 53%, respectively ( 10 , 55 ), which improved the above situation. However, the volume purchase policy at the national level is new, and the number of selected drugs is significantly less than that of drugs in the basic medical insurance catalog. Moreover, the policy does not fully cover retail pharmacies, and prices in pharmacies are generally higher than in hospitals. Therefore, drug prices do not meet the insured's expectations for immediate, low-cost purchases of various drugs, thus significantly affecting their PBBMI.
The interview results showed that some insured complained about the inadequate coverage of the basic medical insurance drug catalog and high-value medical consumables. According to existing research, insufficient coverage was mainly related to a few specific problems. First, China's health technology assessment is weak, which hinders the access of Medicare drugs and consumables ( 56 , 57 ). Second, the dynamic adjustment mechanism of the basic medical insurance catalog is imperfect ( 58 ), and many safe and effective drugs or consumables have not been included in the reimbursable range. Harbin city implements the national medical insurance catalog standard, and the insufficient coverage has increased the medical economic burden and reduced the PBBMI of the insured. Therefore, these problems requires more attention in the future.
18.104.22.168. Insufficient attention to low-income people
This study shows the BMIS does not sufficiently account for the low-income insured, which is reflected in the burden of insurance participation and advancing payment of hospitalization costs.
During the interviews, we found that the low-income insured expressed low PBBMI due to participation costs, which is mainly reflected in the absolute burden of the insured who enrolled for the URRBMI on the payment of participation cost, and the relative burden of the insured who enrolled for the two types of BMIS on the continuous rise of premiums. This finding is consistent with the results of another related study ( 16 ). Compared with the middle and high-income groups, the the low-income insured have faced great objective pressure in making daily payments for food, housing, education, and transportation. To reduce the insured's payment burden, the state has provided a large amount of financial subsidies to URRBMI. For example, in 2021, the financing standard of URRBMI in Harbin was 860 yuan, of which the state financial subsidy was 550 yuan per person. Individuals only needed to pay 310 yuan per year ( 59 ). However, the scarcity of disposable funds causes great economic pressure every time expenditure for the low-income insured is increased ( 38 ). Therefore, the government needs to pay high attention to the absolute burden of this part of the population in policy-making. Additionally, the financing standard of the participation costs of UEBMI and URRBMI shows an upward yearly trend ( 13 , 16 ). Some interviewees with unstable and low income said that compared with previous years, the current higher amount of participation costs made it difficult to accept, and they experienced economic pressure. We further found that when determining the premium increase, these participants do not comprehensively consider some realistic conditions, such as the simultaneous growth of national finance and residents' disposable income. The data showed that the per capita disposable income of Harbin increased by about 34% from 2015 to 2020 ( 60 ). However, the interviewees were unaware of this increase and compared the current premium with the premium from previous years, resulting in a perception of economic burden. To address this way of thinking among the insured, the government may need to share medical insurance information publicity and increase education related to BMIS.
At present, Harbin, like most regions of China, implements the advance payment systems of hospitalization costs, that is, individuals are required to pay a certain amount of medical costs in advance before hospitalization, and then settle the actual amount incurred after the treatment. This study showed that the hospitalization advance payment system increased the hospitalization economic pressure of the low-income insured, consequently affecting their PBBMI. It is difficult for low-income people to suddenly pay a large hospital advance payment. In some cases, they are forced to reduce or abandon medical service ( 61 ). Moreover, the design of the advance payment system also increases the hidden opportunity cost ( 62 ).
22.214.171.124.1 Lack of regulatory measures and support
Providing corresponding regulatory measures and support is an important part of the BMIS. This study shows that the insured think that there are still deficiencies in the supervision of medical institutions and doctors and the supply support of basic medical insurance drugs.
When the interviewees were asked about the problems affecting their perceptions of convenience of medical treatment, most of the insured mentioned the rules of limiting the duration of hospitalization, which also drove their low PBBMI. In Harbin, medical institutions set the standard for the average duration of hospitalization according to national regulations to control unreasonable basic medical insurance expenses and improve the utilization rate of medical resources ( 63 ). However, to obtain higher basic medical insurance compensation, some hospitals and doctors tackle the national regulations by decomposing hospitalization, ostensibly meeting the national standard for the duration of hospitalization ( 64 ). Patients need to bear the time and health cost of repeated discharge and hospitalization, resulting in poor medical experience and low PBBMI. Some scholars believe that these unreasonable medical behaviors are related to the lack of the BMIS regulatory system and regulatory tools ( 65 ). Although Harbin has followed national policies regarding supervision measures such as legislation, multi department coordination, social integrity system construction, and information construction in recent years, these methods are still in the preliminary exploration stage and need to be gradually improved.
Some insured indicated that they had to purchase drugs at retail pharmacies due to the shortage of hospital drugs, which drove their low PBBMI. This is consistent with other research results ( 66 , 67 ). The shortage of basic medical insurance drugs in China is a complex problem with multiple factors. It is affected not only by drug procurement policies, but also by drug distribution and doctors' use habits. In the drug production chain, China's centralized drug procurement policy adopts the strategy of “the lowest price wins.” However, China has less control over the price of raw materials used to produce drugs and requires manufacturers to guarantee the quality of drugs. Accordingly, manufacturers face the risk of production costs outweighing profits and are consequently less motivated ( 68 , 69 ), even abandoning bids or refusing to supply halfway through fulfilling an order. In the drug distribution chain, China implements the principle of exclusive distribution ( 67 ). If the distribution enterprise experiences transportation failure or delay, there will be a direct effect on the purchase and utilization of drugs. Regarding drug use, the ability of Chinese pharmacists to participate in the use of drugs by clinicians is limited, resulting in the lack of correction of some clinicians' irrational medication ( 70 ). These doctors are probably reluctant to use low-cost drugs, making their clinical dosage small, and causing enterprises to be reluctant to produce. Currently, many hospitals in Harbin suffer from a shortage of drugs, especially low-cost drugs ( 71 ). One of the priorities of China's medical reform is ensuring the supply of drugs. China needs to reinforce the guarantee and early warning of drugs, and improve the availability of drugs for participants.
4.2.2. Intuitive cognitive bias
Intuitive cognition is generally considered a free, uncontrolled way of thinking. It can aid in rapid decision-making and saving cognitive resources, but it is affected by people's knowledge reserve, past experiences, and environment, and may therefore produce cognitive bias ( 72 , 73 ). This study shows that when making actual judgments regarding BMIS, the insured usually lack accurate deductive reasoning, and are more affected by three main aspects: non-status quo reference dependence, accessibility heuristics, and representativeness heuristic. This results in the deviation between their overall experience of BMIS and their actual intuitive perceptions, which further affects their PBBMI.
126.96.36.199. Non-status quo reference dependence—Expect the cost of participation will remain the same and hospitalization will be fully reimbursed
The concept of reference point originates from prospect theory proposed by Kahneman and Tversky, which suggests that people have comparative preferences in the process of decision making, potentially encoding the deviation direction and degree between the actual profit and loss of the decision-making result and the psychological central base point (reference point) ( 74 ), and make subsequent judgment accordingly. Generally, the reference point includes the current reference point with the actual situation as the reference and the non-current reference point with no objective situation as the reference, such as the individual's expectation or goal ( 75 ). During interviews, we found that the insured often take the expectation of non-rising participation costs and complete reimbursement of hospitalization costs as the non-current reference point. However, owing to the contradiction between people's growing medical and health care needs and limited BMIS resources, their actual experience is far from their actual expectations. From the perspective of reference, this unsatisfactory comparison result imperceptibly causes the participants to have cognitive bias and misjudge the BMIS, making the insured believe that they have incurred losses and have low perceptions of the recognition and benefits of the public welfare and utility of BMIS ( 76 , 77 ).
188.8.131.52. Availability heuristic—Relatives and friends dependence
The availability heuristic indicates that people tend to rely on information that is easily obtained and can be easily extracted from memory to make estimations and judgments. This method can make the information more accessible, but there is risk of biased judgments due to credulous misinformation ( 78 ). Specifically, we found that the perceptions and judgments of the insured regarding BMIS mostly come from information transmitted by relatives and friends, because this information is vivid and easy to understand and retrieve. However, these people typically do not know all the information related to BMIS, nor is the information they know necessarily true. Additionally, individualized BMIS usage plans are not the same for everyone. This information passed from friends and relatives can affect people's actual experiences and further low PBBMI.
184.108.40.206. Representativeness heuristics—Doctors' omniscient inference
Representativeness heuristics indicates when people judge an event, they often choose representative cases and make inferences from them ( 78 ). We found that the insured have misperceptions about doctors' knowledge of BMIS because doctors generally have extensive medical-related knowledge and higher education ( 79 ). Additionally, doctors have close working knowledge of BMIS, so the insured tend to make conclusions based on their perception that doctors have excellent clinical knowledge and knowledge of BMIS policies. However, BMIS restricts and control doctors' diagnosis and treatment behavior. In recent years, the hospital payment method reforms of China's BMIS have made physicians do not stand the same position as BMIS many times ( 35 ). Additionally, doctors do not necessarily know BMIS details that patients want to know. Enrollees are often affected by the limited information provided by physicians, exaggerating the deficiency of BMIS.
4.2.3. Rational cognitive bias
Compared with intuition, rationality can help people judge things more thoughtfully. However, in a complex social environment, it is impossible for a person to obtain all the information and knowledge relevant to their judgment. Moreover, the human brain has limitations in its ability to comprehend, calculate and analyze things, resulting in people's rational judgments being prone to errors ( 80 ). Our research shows that the main reason of BMIS rational judgment bias among the insured is the lack of information, misinterpretation of information and the misjudgment of roles and functions, resulting in low PBBMI.
220.127.116.11. Lack of information
This study shows that incomplete access to information and information update lag could result in the bias of the rational cognition of the insured, which affects PBBMI. For example, during the interview, we found that insured's information about the restricted payment policy is inadequate, resulting in their misconception that the reimbursement rules for special drugs are the same as those for ordinary drugs. Some insured's understanding of hospitalization reimbursement policy of off-site medical treatment is still in the stage that the hospitalization expenses cannot be settled on time across provinces. These performances of the insured show that there are deficiencies in the way, content, and timing of BMIS information publicity in China ( 16 ). A survey of 970 respondents in Heilongjiang Province on their knowledge of BMIS policies showed that 77.1% had average or no knowledge of the policies ( 81 ), thus confirming the above view. Therefore, effective publicity methods should be actively explored to improve the insured's understanding of BMIS.
18.104.22.168. Misinterpretation of information
Information misinterpretation occurs when the insured make a wrong interpretation of BMIS information. We found that the insured mainly focus on two aspects of misinterpretation: media information and the failure to understand the original intention for the exclusion of drugs from the basic medical insurance list.
This study shows that due to the limitations of knowledge and experience, the insured are easily misled by information publicized by the media. An interviewee of this study said that “the news propaganda indicates that BMIS can reimburse more than 70%, but the actual hospitalization compensation was only ~50%,” which made him very disappointed. The media selectively presents the best policy information or directly exaggerates the policy effect ( 82 ). Unfortunately, this strategy reduces people's evaluation of the policy, because such reports blindly raise people's expectations, and it should therefore be avoided.
The study also shows that some insured had low PBBMI owing to the sudden exclusion of their habitual drugs from the basic medical insurance list. We think that is highly correlated with a lack of understanding of the drug withdrawal mechanism. At the beginning of the 21st century, China's approval of drugs was relatively loose, resulting in the inclusion of many drugs with low cost performance and insufficient curative effects, and replaceable drugs in the list ( 83 ), which not only increased the expenditure pressure of basic medical insurance funds, but also affected the life and health of the insured. Following the reform of the healthcare system, China has gradually began adopting pharmacoeconomic evaluation methods to systematically evaluate and adjust drugs in the list and those intended to be included in the list ( 84 ). Accordingly, a total of 179 drugs have been transferred out of the list from 2019 to 2020 to make space for more good and new drugs ( 85 , 86 ). However, the insured are not familiar with the original purpose of the list adjustment, but are mostly influenced by the increased economic pressure of purchasing drugs at their own expense and the distrust of the treatment effect of low-cost drugs, making them mistakenly believe that the core of the list adjustment is mainly to save basic medical insurance funds rather than for the personal interests of the majority of the insured, further resulting in their low PBBMI.
22.214.171.124. Misjudgment of roles and functions
This study shows that insured relatively misjudged their roles and the functions of a basic medical insurance card.
Participation can increase people's emotional engagement and favorable feeling toward the event they are involved in Reynolds and Beresford ( 87 ). However, this study shows that some insured are relatively indifferent to participating in the supervision of basic medical insurance funds, which may be because they find it difficult to link the consequences of insurance fraud with their own interests. It is not a legal obligation to participate in supervision. However, this attitude indirectly reflects that they do not care enough about the BMIS. The lack of participation and emotional investment will relatively affect their PBBMI.
According to the interviewees' descriptions, we found that some insured's PBBMI was reduced because their basic medical insurance card could not buy non-medical supplies in the drugstore. As in most regions of China, there are certain historical reasons why these interviewees in Harbin hold this misunderstanding; China's regulatory system is not perfect and lacks supporting regulatory technical support ( 88 ). Many pharmacies long used the method of drug exchange to meet their own interests and the needs of the insured ( 89 ). This behavior amplified the insured's misperception of the payment function of the basic medical insurance card. Following China's crackdown on insurance fraud, the arbitrage of basic medical insurance cards by designated pharmacies has been effectively improved. However, the misperception among the insured is difficult to change in a short time.
4.2.4. System environmental impact
PBBMI is a comprehensive perception formed by the integrated effects of various results. It includes not only the direct perceptions driven by the use of BMIS, but also the indirect perceptions generated by the system environment. In the interview, we found that when the insured evaluate PBBMI, it is easy to generate a halo effect ( 90 ). In other words, the insured will perceive the entire health system including BMIS according to their bad impression of other elements in the system environment.
126.96.36.199. Poor performance of the health system
Our research shows that the important health system factors that indirectly affect PBBMI are doctors' poor work attitudes and defective hierarchical medical systems. Regarding doctors' attitudes, relevant studies indicate that Chinese doctors experience significant work pressure ( 91 ), lack of cultivation of humanistic quality and communication skills ( 92 ) and patients' distrust ( 35 ), which indeed drive some doctors' poor work attitudes ( 93 , 94 ). Additionally, regarding the construction of hierarchical medical systems, owing to the imperfect differentiated reimbursement policy of BMIS and the inadequate capacity of primary care institutions ( 95 , 96 ), many insured individuals in Harbin with common or frequently-occurring diseases still choose to go to large, overcrowded hospitals ( 97 ). These factors may have affected the insured's experience and negatively influenced their PBBMI.
188.8.131.52. The elderly have difficulty adapting to the development of information technology in health care
Notably, the data survey in this study coincides with the outbreak of COVID-19. In this period, hospitals in all provinces and cities in China took many measures to manage the spread of COVID-19 and the gathering of people in hospitals, such as the widespread use of network appointment registration, online payment, and electronic report forms. However, these measures have inconvenienced the digital poor, represented in large part by the elderly. The elderly have considerable demand for medical services. However, China's Internet users aged 60 and over account for only 11.2% of the total relevant population; their information technology utilization rate is low ( 98 ). Moreover, the decline of physical function and the difficulty of accepting new things make it difficult for the elderly to use medical services in an information-based medical environment ( 99 ). The technological marginalization of the elderly results in poor experience of medical services. The effect of medical information technology on their health care utilization will shift and further influence their evaluation of BMIS and reduce the PBBMI.
The reform of China's BMIS is progressing vigorously. However, this study shows that 44% of the insured still have low PBBMI, and that the problem and obstacles for PBBMI mainly focus on the design of the BMIS, intuitive cognitive bias, rational cognitive bias, and system environmental influences.
Therefore, based on the quantitative and qualitative research results, the study indicates that to improve the insured's PBBMI, the government should focus on the insured who enroll for the URRBMI, receive low-income, lack recent basic medical insurance use experience and are elderly. Additionally, at the problem level, the obstacles should be eliminated from three aspects: improving the BMIS policy, reducing the cognitive bias of the insured and optimizing the health system environment. Regarding the policy of BMIS, the government should adjust drug prices, optimize the health technology evaluation, expand the scope of BMIS reimbursement, reduce the participation costs and advancing hospitalization costs for low-income people, reinforce the supervision of the basic medical insurance fund, and improve the drug supply guarantee mechanism to improve the service quality of BMIS. Regarding the intuitive and rational cognitive bias of the insured, the government should improve the publicity channels, contents, forms, and feedback channels of BMIS information to improve the cognitive of the insured. Moreover, the government can cooperate with the media platform to ascertain and clarify false information. Regarding, the health system environment, the government should adjust the workload of doctors, reinforce the cultivation of doctors' humanistic quality, and improve the construction of the hierarchical treatment system to improve the insured's medical experience. Additionally, these institutions that are related to BMIS should pay more attention to the actual difficulties of the elderly when providing services.
This study had several limitations. First, owing to the effect of the COVID-19, the participation of rural migrants and respondents with recent basic medical insurance use experience was low. Second, due to resource and condition constraints, the quantitative survey sample can only represent the situation of Harbin, and cannot be extrapolated to the whole country. To reflect the situation in China as a whole, further studies involving a wider range of regions and populations are needed. Having acknowledged these limitations however, we are of the view that results and findings from this study provides a reference for other areas where the cause of medical insurance is undergoing reform.
Data availability statement
The original contributions presented in the study are included in the article/ Supplementary material , further inquiries can be directed to the corresponding authors.
PW, LS, YL, and QW: conceptualization and manuscript revision. MJ, NN, and LG: data collection. PW, SL, and ZW: data analysis and original draft writing. PW, YZ, and WH: interpretation of data. All authors read and approved the final manuscript.
This work was supported by the National Natural Science Foundation of China (Grant No. 71804036), the training plan for young innovative talents in ordinary undergraduate colleges and universities in Heilongjiang Province (Grant No. UNPYSCT-2020176), the China Postdoctoral Science Foundation (Grant No. LBH-Z17156), the National Natural Science Foundation of China (Grant Nos. 72174047 and 72174049), and the Social Science Foundation of China (Grant No. 19AZD013).
The authors would like to thank all the participants in this study.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpubh.2023.1043153/full#supplementary-material
BMIS, Basic medical insurance systems; UEBMI, The urban employee basic medical insurance; URBMI, The urban resident basic medical insurance; NCMS, New rural cooperative medical scheme; URRBMI, The urban and rural resident basic medical insurance; OOPE, Out of pocket expenditures; OECD, Organization for Economic Co-operation and Development; PBBMI, The perceptions of the benefits of the basic medical insurance system; ORs, odds ratios; IPA, Interpretative phenomenological analysis.
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Keywords: basic medical insurance system, insured, perceptions of benefits, policy literacy, mixed methods research
Citation: Wang P, Li S, Wang Z, Jiao M, Zhang Y, Huang W, Ning N, Gao L, Shan L, Li Y and Wu Q (2023) Perceptions of the benefits of the basic medical insurance system among the insured: a mixed methods research of a northern city in China. Front. Public Health 11:1043153. doi: 10.3389/fpubh.2023.1043153
Received: 29 September 2022; Accepted: 30 March 2023; Published: 17 April 2023.
Copyright © 2023 Wang, Li, Wang, Jiao, Zhang, Huang, Ning, Gao, Shan, Li and Wu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Linghan Shan, firstname.lastname@example.org ; Ye Li, email@example.com ; Qunhong Wu, firstname.lastname@example.org
† These authors have contributed equally to this work
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Study on awareness of health insurance among the adult population of rural areas in the Wardha district of Maharashtra: a protocol
Sudha Rani Bhoi Roles: Conceptualization, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing Nikhilesh Nagtode Roles: Supervision
This article is included in the Datta Meghe Institute of Higher Education and Research collection.
Health insurance awareness, Rural, Health insurance coverage, Health insurance, awareness
Globally, the foundation of health insurance is substantial. However, the Indian health insurance sector lags. Every year, a substantial number of Indians, primarily women and children, die due to a shortage of healthcare facilities, particularly in rural areas which are home to the majority of Indians. Most of these rural residents live below the poverty line and cannot afford medical expenditures. Obtaining health insurance is a difficult task for these people. 1
An individual or group can pre-pay for healthcare with the help of health insurance by paying a premium. For the rural poor, health insurance provides a rapidly progressing social security strategy, whose ability to make an income is seriously threatened by chronic health issues brought on by the prevalence of diseases and lack of access to affordable healthcare. 2 , 3 B ut according to the 75th round of the National Sample Survey (NSS), only 14.1% of the rural population is covered by health insurance in its report “Key Indicators of Social Consumption in India: Health” (July 2017 – June 2018). 4 Therefore 85.9% of individuals in rural India still lack health expenditure coverage and thus are uninsured. T hese findings reflect that a more significant proportion of the rural population is unaware of health insurance and its associated benefits. 5
The predominant causes of morbidity, particularly in rural regions, are infectious, communicable, and waterborne illnesses, respiratory infections, pneumonia, and genital tract infections. Cancer, blindness, mental health issues, high blood pressure, diabetes, HIV/AIDS, accidents, and injuries are among the non-communicable diseases on the rise. 6 R ural communities are especially vulnerable to distressing health financing due to their distinctive socioeconomic situations, like the inability to afford healthcare expenditures at an unforeseen time. 7 For the same reason, it is necessary to give low-income families financial protection. Health insurance may be a means to lower expenditure and make it more accessible to high-quality healthcare. 8 , 9 E ven if a population uses either private or public health systems, the policymakers want to provide health insurance to them to reduce their out-of-pocket expenditures and assist them in achieving the majority of universal health coverage. 10
In India, the state and central governments offer numerous health insurance programs. Through the process of comprehensive coverage along with affordable premiums, these programs aim to offer health insurance to all Indians. The Mahatma Jyoti Rao Phule Jan Arogya Yojana (MJPJAY) ( https://www.jeevandayee.gov.in/ ), Ayushman Bharat-Pradhan Mantri Jan Arogya Yojana (AB-PMJAY) ( https://nha.gov.in/PM-JAY ), and Central Government Health Scheme (CGHS) ( https://cghs.nic.in/ ) are a few of the government-sponsored health insurance plans. On April 1, 2020, the MJPJAY and ABPMJAY were introduced in the state of Maharashtra. 11 Health insurance is provided to beneficiaries in the insurance mode by United India Insurance Company Limited and in the assurance mode by the State Health Assurance Society. On behalf of the eligible households, the State Health Assurance Society pays an insurance premium of Rs.797/- per family annually to the insurance firm in quarterly instalments. 12
T he ultimate sustainable development goal (SDG) Target 3.8 is to achieve universal health coverage (UHC) including financial risk protection and affordable quality health care services and medicine for all. These efforts are strategies to promote health equity by reducing disparities among individuals across the globe. Hence rural individuals must be aware of and use the abovementioned health insurance programs.
Despite all the efforts made by the health system to assist the Indian population with supporting their healthcare finances, the public is unable to use these services simply because they are unaware of them. Additionally, due to poverty and a lack of education, most rural population needs to gain first-hand knowledge about health services compared to their urban counterparts. The idea that healthy people don’t need health insurance is one of the most widespread misconceptions about acquiring it.
India’s rural population needs to be informed that health insurance can assist them in managing their finances when using healthcare services. A robust health insurance plan will be most beneficial for reducing the burden of health care expenses in an emergency. However, this will only be achievable if the rural Indian population is aware of these advantages, enabling them to use services like health insurance to lower their out-of-pocket health expenses and solve health-associated poverty.
Aim and objective
The aim of this study will be to assess the level of awareness about health insurance among the adult population in rural areas in Wardha district of Maharashtra.
1) To study rural community individuals’ awareness, perception, and utilisation of health insurance
2) To determine the association between socio demographic variables and awareness of health insurance
This research proposal has been approved by the Institutional Ethics Committee of the Datta Meghe Institute of Higher Education and Research (Deemed to be University) (Ref. No. DMIHER (DU)/IEC/2023/648). We will obtain written informed consent from all the participants before beginning the data collection procedure, which includes the study’s objective. We will give them a thorough explanation of the survey so they understand it is being done only for research purposes. Anonymity and confidentiality of participant responses and data will be maintained. We will ensure that the interviewee has privacy and feel comfortable throughout the interview.
The present study will be a community based cross-sectional observational study design. The proposed study would take place in the Wardha district of Maharashtra, in the rural field practice area under the Department of Community Medicine, at Jawaharlal Nehru Medical College (JNMC), Datta Meghe Institute of Higher Education and Research (DMIHER).
The selection of villages for this study will be based on the convenience sampling method. The inclusion criteria will be those individuals within the region who are above the age of 18 and were at home during the house-to-house visit. The Adhaar card, a government issued proof of identify document, will be used as identification of study participants. The exclusion criteria will be individuals who do not give consent to take part in the study, those individuals who did not meet the age requirement, and any empty households during the visits.
1. Socio demographic variables
• Name, age groups (in years), gender, type of the family, religion, occupation, education, marital status, and socioeconomic status of the participants.
2. Health insurance awareness among participants
• Percentage of participants aware about health insurance,
• Source of information regarding health insurance,
• Preferences on the type of health insurance,
• Reasons to take up health insurance; and
• Reasons for not enrolling in health insurance.
A pre-designed and semi structured questionnaire will be used. The questionnaire has been developed by the authors and has not been piloted yet. It will be piloted before start of the study and will be in both English and the regional language i.e., Marathi/Hindi. It has two sections: section A includes questions about the participant’s socio-demographic characteristics, whereas section B includes questions about the participant’s awareness of health insurance.
House-to-house visits will be done within the selected region and information will be obtained by interview. The interview will be held in either Marathi or Hindi and the consent of the participants will be recorded in a signed consent form. The KoboToolbox software will be used for data collection during interview.
To maintain anonymity, we will use numbers for participants on all the research records and documents. The data will be entered in Microsoft Excel 365 and analysed by SPSS 24.0 Software. Descriptive statistics such as frequency and percentages will be calculated. Association between variables will be calculated using Chi-Square Test including the sociodemographic variable and health insurance awareness. The data thus analysed will be presented in tables, graphs, and charts.
A participant may be unable to recollect details about his or her usual health expenditures leading to recall bias. In order to avoid this bias, we can ask participants to look at their recent medical bills for references.
During participant responses to various study variables, information bias may emerge. So, if any questions are unclear to participants, we will clarify them throughout the interview.
Alpha ( α ) = 0.05
Estimated proportion ( p ) = 0.81
Estimated error ( d ) = 0.05
Sample size = 237
The results of this study will involve an assessment of the participant’s awareness, perception, and utilisation of health insurance. These results will be calculated in frequency and percentage using descriptive statistics and the association between socio demographic variables and awareness of health insurance will be calculated using chi-square test.
Gowda et al., did a study named ‘Awareness about health insurance in rural population of South India’ in 2014. 13 The study objective was to evaluate the sociodemographic features of respondents and to assess the level of health insurance awareness among rural residents. 81% of the participants in this survey knew about health insurance, as per the results of this study. But this study also came to the conclusion that among them, utilization of health insurance was low and the respondents needed to be educated on behaviour change.
In 2013, Bansal et al., carried out another study with the title ‘A community-based study to assess the awareness of health insurance among rural Northern Indian population’. 2 The setting for this study was rural Uttar Pradesh. The study’s main objective was to determine how much rural individuals in Northern India knew about health insurance. Study outcomes suggested that 55.5% of study participants had never heard of health insurance, compared to 44.5% who were generally aware of it. Conclusions from the research revealed that among the study subjects, health insurance awareness was currently not widely prevalent.
A community-based study entitled ‘Awareness and perception of health insurance among rural population in Kancheepuram district, Tamil Nadu’ was conducted in 2019 by Raja T. K. et al. 14 This study’s objective was to evaluate rural residents’ knowledge, use, and attitudes toward health insurance. Study results showed that, health insurance awareness was seen in 51% of participants, with television being the most primary source of information.
In 2015, Indumathi K et al., conducted a study entitled ‘Awareness of health insurance in a rural population of Bangalore, India’. 8 Analysis of the study population’s awareness of health insurance as well as the study of its socioeconomic and demographic features was among the study’s primary objectives. The study’s findings indicated that while 24.3% of participants were unaware of health insurance, 75.7% of the participants had some knowledge of it. This study concluded that, it is necessary to start effective Information, Education, and Communication (IEC) programs to raise awareness of the importance of health insurance.
This cross-sectional study will be conducted only in selected villages of Wardha district of Maharashtra. Thus, the findings of the study may not be compared to results related to a geographical area with a larger population and external validity of this research would be limited.
Lack of health insurance in rural areas is one of the barriers to healthcare access. Individuals without health insurance will experience impoverishment due to health-related problems. Increased health insurance awareness can also be an indicator of universal health coverage. The awareness, perception, and utilization of health insurance among people in rural communities will be evaluated in this study. With the help of this study, we can gain an understanding of this aspect in rural areas.
No data associated with this article.
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